PP injected to a server which echos them back to the sender. ... VPS: VPS probing measures the capacity along end-to-end path for each hop using the.
Survey of Bandwidth Estimation Techniques in Communication Networks Shilpa Shashikant Chaudhari1 , Rajashekhar C. Biradar2 1 Department of Electronics and Communication Engineering 2 Department of Information Science and Engineering Wireless Information Systems Research Laboratory Reva Institute of Technology and Management, Bangalore-560 064, India E-mail:{shilpachaudhari, raj.biradar}@revainstitution.org
Abstract Increased popularity and wealth-creation potential of conventional mobile phones, smart phones and tablet computers resulted in the unprecedented penetration of wired and wireless communications. Most of such real-time applications require video streaming that needs Quality of Service (QoS) provisioning in addition to good end-to-end transport performance in network infrastructure. Estimating the reliability of an end-to-end network path is critically important for such applications. Available bandwidth at a node is an important QoS characteristic of the path that is a minimum spare capacity of links constituting a network path. Researchers have proposed various techniques for estimating the bandwidth to increase the network throughput. In the past, Bandwidth Estimation (BE) techniques developed for wired networks are focused on point-to point dedicated links that are not directly usable in wireless networks. The reason is that these techniques are based on models which are no longer valid in wireless environment. This paper aims to provide a comprehensive survey of the BE techniques proposed till date by researchers in the literature for both wired and wireless networks. We have categorized the BE techniques into four main categories as active probing techniques, passive techniques, techniques only for wireless networks and other BE techniques. A brief outline of each technique is discussed which includes the problem statement, operation methodology, results and applications. Techniques in each category have been compared using various parameters such as accuracy, BE time and overhead. Keywords: Available bandwidth estimation techniques, Active probing, Passive techniques, Cross-layer techniques and Model based techniques
1
Introduction
The wireless network has increased popularity immensely in conventional mobile phones, smart phones and tablet computers over the last two decades. The unprecedented penetration of 1
wireless communications investigates new technologies that might transpire and change things in the future[1]. Wireless networks have been developed with improved performance in terms of delay, bandwidth consumption, jitter, etc. However, the ever-increasing demand for the realtime multimedia applications to reach remote wireless devices constrained the growth of wireless networks due to various factors such as limited bandwidth, limited size of mobile devices,the delayinvolved in processing, and communication with other devices, etc. These applications require huge amount of bit rate and Quality-of-Service (QoS) support. QoS provisioning improves the end-to-end performance in such heavily loaded network through QoS-aware routing, admission control, resource reservation, traffic analysis and scheduling. One of the most crucialcomponents of a system for QoS provisioning is to estimate the state of the networks resources and thereby decide which application data can be processed. Estimating network resources such as bandwidth in such heavily loaded wireless network is a non-trivial task due to the aforementioned factors of wireless network.
1.1
Significance of Bandwidth Estimation in Communication Networks
Bandwidth Estimation (BE) is a vital component of admission control for QoS in both wire-line as well as wireless networks. The bandwidth related metrics are link/path capacity, Available BandWidth (ABW) and Bulk Transfer Capacity (BTC)[2]. Link capacity and available bandwidth are defined both for individual links and end-to-end paths, while BTC is usually defined only for an end-to-end path. The maximum possible bandwidth that a link or a path can deliver is called as the capacity. The maximum unused bandwidth at a link or a path is called available bandwidth (measured in bits per second) that refers to the speed of bit transmission in a channel or on a link. The achievable throughput of TCP connection during bulk transfer is called BTC. The measured link capacity on wireless links does not only depend on the packet size that is critical to the bandwidth measurement, but also on the cross-traffic intensity[3]. The entire capacity of a channel is not used during packet transmission. The amount of bandwidth is also required for communication related overheads such as initiating communication, neighbor node interference. In wireless networks, the available bandwidth undergoes a fast time-scale variations due to channel fading and error from physical obstacles. These effects are not present in wireline networks and therefore it becomes a challenging task to estimate the bandwidth in wireless networks. Furthermore, the wireless channel is also a shared-access medium and the available bandwidth varies with the number of hosts contending for the channel[4]. Available bandwidth is used for analyzing network performance and optimizing end-to-end transport performance. It is also used to improve the QoS of multimedia services and video streaming over a network as such applications require large bandwidth. Both analysis and test-bed results given in [5] have shown that the current measurement paradigms cannot be used off-the-shelf in large-scale deployments. All techniques underestimate the available bandwidth significantly as additional overhead, mutual interference and intrusiveness impair the estimations and overall performance of the network.
2
A literature search on the topic of this paper is identified in [12] and [6] which includes overview of only active probing techniques and only wired BE techniques respectively. Authors of [12] and [6] does not consider any technique related to wireless techniques. The abundance of diverse BE techniques together with the lack of wide-ranging surveys provide the motivation for the work in hand. Even though a large body of work has been published of BE to achieve QoS till date, no wide-ranging survey of these approaches exists at the time of writing this paper. In this paper, we consider BE techniques that are designed for wired as well as wireless network including mobile ad-hoc network. This survey serves as a basic platform for researchers to explore in this area.
1.2
Terminologies used in Bandwidth Estimation Techniques
As most of the other techniques, BE techniques can be categorised in several ways based on one of the following. (1) Type of technique used (2) Number of Probe Packet (PP) sent, (3) Network type, (4) Hybrid/mixed approaches. While studying the operation of the various protocols, it became clear that they were not easy to categorize with unique name. In this section, various tags used by researchers for classification of BE techniques are listed. 1.2.1
Terminologies with Type of Technique
Most of the researcher classifies BE techniques into passive, active and mathematical model based categories for wired or wireless networks, but some has their own tags given for the same role. Examples are given below. • Passive (i.e. non-intrusive) estimation and active (i.e. intrusive) probing[9][10][11][12][13][14] [15][16].
• Active probing, mathematical model based and calculation based passive measurement techniques[17][18].
• Self-congestion and model-based techniques[21]. • Algorithms that are designed for specific networks usually with guaranteed QoS, Algorithms that use PP with pre-determined spacing and Algorithms targeting video streaming where a client-server model is assumed[23]. Tags given by the researcher for the same role of BE put the new researcher in dilemma that the technique with different tag is having the same role or different, but in fact they are the same with different names. Eg. (1) Algorithms that are designed for specific networks usually with guaranteed QoS and calculation based passive measurement techniques are passive measurement technique. (2) Algorithms that use PP with pre-determined spacing and selfcongestion techniques are active probing technique. (3) Mathematical model based technique is model-based techniques. The role of these techniques is as follows.
3
• Active probing techniques: It sends PP through the network at multiple traffic rates and
available bandwidth is estimated based on the variation of the end-to-end PP inter-arrival time at destination node[17][37].
• Passive techniques: It uses local information such as congestion situation, packet loss and
delay on the used bandwidth by passively monitoring the communication medium over a certain period of time and find out the channel usage ratio that is used to estimate available bandwidth without any impact to the existing flows[37][17][39].
• Mathematical model based techniques: Markov model and effective link model enable accurate calculation of available bandwidth without affecting any existing flows[17][39].
1.2.2
Terminologies for Number of Probe Packet
The concept of PP is used only in active mechanisms. Most of the researcher classifies active BE techniques into two categories such as single-packet/one-packet and packet-pair techniques[6][7][8] but others classifies them into following categories for similar role confusing new beneficiary. • Isolated probing, direct probing and iterative probing techniques[5]. • Direct probing and iterative probing techniques[19]. • Direct probing, iterative probing and mixed techniques[20]. • Packet Dispersion Measurement (PDM), Probe Gap Model (PGM) and Probe Rate Model (PRM)[22].
• PRM and PGM[10][24] [25][26][27][28][29][30][31][32][33][34][35][15][16]. • Variable Packet Size (VPS) probing, Packet Pair/Train Dispersion (PPTD), Self-Loading Periodic Streams (SLoPS) and Trains of Packet Pairs (TOPP)[2].
Isolated probing and probe delay model are single-packet probing technique[5]. PRM and iterative probing are self-loading techniques[36]. PGM and direct probing are packet-pair dispersion techniques[36]. It is discovered that functionality of self-congestion technique and PDM is similar to active probing technique. The roles of these techniques are as follows. • Single-packet probing techniques: These techniques use PP with single-packet. In this
technique, only one PP at a time is injected to measure delay. Next PP injected with pre-defined time interval into the network path. It measures the link capacity by acquiring the time difference between the round trip time from one end of the link to the other[6][8].
• Packet-pair probing techniques: These techniques use PP with two packets called Backto-back packets or packet pairs. PP injected to a server which echos them back to the
sender. The spacing between the packet pair is determined by the bottleneck link and is preserved by higher-bandwidth links[6]. 4
• VPS: VPS probing measures the capacity along end-to-end path for each hop using the
Round Trip Time (RTT) from the source to each hop of the path as a function of the PP size[38].
• PRM: It is based on the concept of self-induced congestion by PP trains at various rates. Rate greater than the BE increases the queuing delay and, therefore, reduces the output rate. It determines whether the input rate exceeds the BE by sending a stream of packets and studying the behaviour of the queuing delay or measuring the output rate. The PP stream is sent iteratively, sometimes changing some of its parameters, in order to be more accurate[20]. • PGM: BE is difference between input and output time gaps of the packet pairs. If the
tight-link capacity is known, an estimation of the BE is obtained by measuring the output rate or estimating the cross-traffic rate[20].
• Mixed techniques: It combines both direct/PGM and iterative/PRM probing[20]. • TOPP: TOPP probe the network with trains of packets at an increasing rate. The rate is
changed by modifying the input gap of each pair. BE is estimated as the maximum input rate not larger than the measured rate at the destination[2].
Various tools have been developed to check the performance of these techniques. Tools based on isolated probing are delay based tools, direct probing are dispersion-based tools and iterative probing are congestion-based tools. 1.2.3
Terminologies for Network Type
BE techniques based on type of network are classified into BE techniques for wired and BE techniques wireless networks. In [40], these techniques are classified into three categories as wired network, wireless network and packet dispersion BE techniques. • Wired BE techniques further classified into two categories: passive and active probing techniques[12].
• Wireless BE techniques further classified as given below. – Probing techniques, cross layer-based techniques and model-based techniques[41][12]. – Probing-based technique and cross-layer technique[42]. – Measurement-based approaches, model-based approaches and calculation-based approaches[39]. – Proactive measurement and reactive measurement[43]. It is discovered that packet dispersion is packet-pair technique. Probing techniques, measurementbased approaches and reactive measurement are active probing techniques. Model-based techniques are mathematical model based technique. Calculation-based approaches and proactive measurement are passive techniques. 5
Probing techniques need significant bandwidth resources over the network and cross layerbased techniques require modifications in the protocol. Probing techniques rely on probing traffic that impacts the communication services due to the additional data introduced. Significantly, cross-layer techniques have lower overhead than packet dispersion solutions. However, they are difficult to be deployed widely due to the modifications required in the devices and standard protocols. Most of the existing wireless BE solutions focus on either probing techniques or cross-layer techniques and require either significant bandwidth resources or protocol modifications. To alleviate these problems, model-based technique[41] is used to estimate available bandwidth. The roles of these techniques are as follows. • Cross layer-based techniques: It requires the modifications in the devices and standard protocols[4][44].
• Measurement-based approaches: It is based on PRM/PGM which add high control overload at the expense of the QoS of ongoing applications[39].
• Proactive measurement: It is passive technique for wireless network where any mobile
node monitors packets sent by its neighbours and collects bandwidth related information such as packet size, burst duration to calculate the available bandwidth[43].
• Reactive measurement: It is probing technique for wireless network where two back-to-
back PPs is sent by the source node that travels through the bottleneck link. The delay in the bottleneck link is represented as the gap between these two packets that is used to estimate the bottleneck bandwidth[43].
1.2.4
Hybrid/mixed Approaches
The results compared in [28] demonstrate that BE techniques and tools to estimate available bandwidth are far from being ready to be applied in all the applications and scenarios. The solution to this, researchers have tried to combine more than one BE techniques to overcome the limitation of individual techniques. Such a combined technique is called as hybrid/mixed technique. In this survey paper, we list the relevant BE techniques that were found in the literature, propose a taxonomy for classifying them, discusses the brief outline of each technique, tabulates their main features, and illustrate their classification according to the category adopted for this survey.
1.3
Taxonomy of Bandwidth Estimation Techniques
A survey of BE techniques is required to avoid the tagging confusion as the number of proposed solutions is now fairly large. In this paper, 55 BE techniques are classified to avoid the confusion related to categorization. We developed taxonomy for the BE techniques published in the literature till-date as given in Figure 1. We use the names given by their respective authors if it is named in the proposing paper else the protocols are named on their features in this paper 6
as shown in Table 1. The full and abbreviated names are adopted throughout this paper are given in taxonomy in Figure 1. We divided the BE techniques into four main categories: Active Probing Techniques (APT), Passive Techniques (PT), Techniques Only for Wireless Networks (TOWN) and Other BE Techniques (OBET). BE Techniques
APT
PT
TOWN
OBET
APT
SPAPT
PPAPT VPS PPTDAPT
SLPPAPT
RAPT
HPPAPT
IGI
BART
GNAPP
MR−BART
NCPPD
MiBT
SLDRT
bTrack
DACME
PAB
AABE
RBM
TWABE
NFE
SLoPS TOPp
PATHCOS++
PTP
OBET
TOWN
PT
ImTCP GPT
CLT
PPT
MBT
BEMV
EEBRAL
QOS−AODV
SDSBE
MEEBEMHN
SABE
APBE
TCPV
BRuIT
iBE
BETCPDT
TREND
ABEWN
TCPW
CACP
TIBET
STABE
FBETNAS
DABE
AAC
QBEM
CLDEBE
MBE
ABE
HSBE
PPRCH
DBM
IAB
cPEAB
ABE11WN
BER
ARCH AWMM
DCSPT
Figure 1: Taxonomy for BE Techniques APT is further categorized as Single Packet APT (SPAPT) and Packet Pair APT (PPAPT). PPAPT is sub-divided into four categories as Self Loading PPAPT (SLPPAPT), Packet Pair/Train Dispersion APT (PPTDAPT), Reactive PPAPT (RPPAPT) for wireless network and Hybrid PPAPT (HPPAPT). PT is further categorized as Generic PT (GPT) and Proactive PT (PPT) for wireless network. TOWN is classified as Cross-Layer Techniques (CLT) and Model-based Techniques (MBT). Some techniques given in the literature does not fit in any of these categories and therefore, we created a separate category as OBET. In this paper, our intention is to provide an overview of all types of BE techniques related to wired as well as wireless networks. Description of each surveyed technique listed in Figure 1 includes the problem statement,
7
Table 1: Acronyms used for Taxonomy Diagram Acronym BE WN APT PT
Technique Bandwidth Estimation Wireless Network Active Probing Techniques Passive Techniques
Acronym NFE DCSPT PPRCH EABRRL
TOWN OBET SPAPT PPAPT GPT PPT CLT MBT SLPPAPT PPTDAPT RPPAPT HPPAPTRAPT VPS SLoPS TOPP PTP SLDRT PAB IGI GNAPP NLCPPD bTrack AABE TWABE BART MR-BART MBT DACME RBM ARCH FBETNAS
Techniques Only for WN Other BE Techniques Single Packet APT Packet Pair APT Generic PT Proactive PT Cross-Layer Techniques Model-based Techniques Self Loading PPAPT Packet Pair/Train Dispersion APT Reactive PPAPT Hybrid PPAPT Variable Packet Size Probing Self-Loading Periodic Streams Trains of Packet Pairs Packet Train Pair Self-Loading Decreasing Rate Train Probabilistic Available Bandwidth Initial Gap Increasing Available Bandwidth Analysis with Gaps of Non-Adjacent PP Estimating Network Link Characteristics using Packet-Pair Dispersion Feedback-assisted robust estimation of available bandwidth Adaptive available BE Two-Way Available BE Bandwidth Available in Real-Time Multi-Rate available BE in Real-Time Minimal Backlogging Techniques Distributed Admission Control for MANET Environments Reactive Bandwidth Measurement in 802.11 Networks AutoRegressive Conditional Heteroscedasticity Time-series Model Fast Required BE Technique for Network Adaptive Streaming
Technique Neuro-Fuzzy Estimator Dual-Carrier Sense with Parallel Transmission Packet Probing with RTS/CTS Handshake Estimation of Available Bandwidth Ratio of a Remote Link or Path Segments TCPV TCP Vegas TCPW TCP Westwood QoS-AODV QoS Enabled Routing in MANETs BRuIT Bandwidth Reservation under InTerferences influence CACP Contention-Aware Admission Control Protocol AAC Adaptive Admission Control ABE Available BE IAB Improved Available Bandwidth cPEAB Cognitive Passive Estimation of Available Bandwidth APBE Accurate Passive BE ABEWN Agent based BE in WN DABE Distributed Available BE QBEM QoS-Aware BE for MANETs DBE Dual BE HSBE Highly Scalable BE ABE11NW Available BE in IEEE 802.11-based WN BER BE based on Retransmission SDSBE Service Differentiation Supported BE iBE Intelligent BE TIBET Time Intervals based BE Technique CLDCBE Cross-layer Designed Effective BE MEEBEMHN Model Based End-to-End BE in Multi-hop Network BETCPDT BE of IEEE 802.11 TCP Data Transmissions STABE System-Theoretic Approach to BE MBE Model-based BE algorithm DBM Delay-based model ImTCP Inline measurement TCP BEMV BE for Multiplexed Videos SABE Statistical Aggregate BE AWMM Adaptive Wavelet-based Multifractal Model SABE Statistical Aggregate BE
operation methodology, results, and applications. Techniques in each category has been compared using various parameters such as accuracy, BE time and overhead. Accuracy depicts how close the measurement results compare to the real network. BE time is the time taken to give meaningful results. Overhead is calculated as the additional number of PP transmitted into the network required to estimate the bandwidth.
1.4
Organization of the Paper
The rest of this paper is organized as follows. Section 2 provides discussion on relevant APT found in the literature. Following this, Section 3 describes relevant PT found in the literature. Description of TOWN techniques is given in section 4. The section 5 explains OBET which does not fall in any of APT/PT/TOWN. We conclude the paper with research contributions in Section 6.
2
Active Probing Techniques (APT)
The dummy packets called PP are sent through the network at multiple traffic rates from sender node to receiver node. The available bandwidth along a path is estimated by measuring the packet inter-arrival times. The available bandwidth at an overlay link must be measured using APT[45]. These techniques add probing traffic, which may degrade the performance of existing flows. The aim of APT is to understand the network characteristics with the help of transmitted PP. Published BE techniques has misconceptions related to basic statistics (such as the impact 8
of the population variance on the sample mean, the variability of the available bandwidth in different time scales and the effect of the probing duration) and the queuing model. Non-linear chaotic traffic or the presence of multiple bottlenecks causes significant underestimation errors that are ignored in APT[35]. In this section, we discuss the published APT in SPAPT and PPAPT subcategory.
2.1
Single-Packet APT (SPAPT)
One PP at a time is injected to measure delay in SPAPT. Each PP is injected with predefined time interval into the network path. It measures link capacity instead of end-to-end path capacity using the time difference between the RTT in PP from one end of the link to the other end[8]. The transmission time t of a packet can be determined as t = (P/b) + l where P is the packet size, b is the bandwidth of the link and l is the fixed latency. If the time (considered as RTT in the PP) and PP size are known, then the bandwidth can be calculated for given fixed latency of a particular link. Tools developed using SPAPT includes pathchar[46], clink[47][48], pchar[49] and tailgating[50]. VPS probing given in Section 2.1.1 is the only technique implemented to estimate bandwidth using single PP. 2.1.1
Variable Packet Size (VPS) Probing
Problem Statement : VPS probing[38] measures the capacity along the end-to-end path using the RTT from the source to each hop along the path as a function of the PP size. Methodology : PP of a given size is sent from the sending host to the network layer of each hop along the path continuously at pre-defined intervals. Time-to-live (TTL) field of the IP header in PP is used to force PP to expire at a targeted hop. Internet Control Message Protocol (ICMP) time-exceeded error messages are sent back to the source from each hop along the path. The RTT in PP of the targeted hop consists of three delay components in the forward and reverse paths: serialization delays Ds , propagation delays and queuing delays. The serialization delay of a packet is the time to transmit it on the link. It is computed as Ds = L/C where L is the size of packet transmitted over a link having transmission rate C[2]. The RTT delay is approximated by gamma distribution. The propagation delay Dp of a packet transmitted over a link is the time taken for each bit of the packet to traverse the link. It is independent of the packet size. Finally, queuing delays Dq can occur in the buffers of routers or switches when there is contention at the input or output ports of these devices. VPS assumes that at least one of the PP together with the ICMP reply that it generates will not encounter any queuing delays. Therefore, the minimum RTT of PP consists of two terms: propagation delays and serialization delays at each link along the packet path. The capacity estimate at each hop i along the forward path is calculated as a function of RTT and size of PP. VPS measures the entire network path capacity at each hop without any special software on both the source and destination. The effects caused by crossing traffic are mitigated because VPS sends a large number of PP and records the minimum traversal times. The large number of PP generated adds considerable stress and interference to the network path that yield significant capacity 9
underestimation errors if the measured path includes store-and-forward layer-2 switches. Results: Two different hops measuring minimum RTT with its own non-linear behavior causes capacity estimation errors due to several factors such as limited clock resolution at probing host, layer two device latencies, fragmentation, cross-traffic and routing changes. The result analysis given by the authors shows that the RTT delay increases linearly with the packet size as well as the number of IP hop. Applications : VPS is used to measure per hop capacity in several network paths.
2.2
Packet-Pair APT (PPAPT)
Two packets called as a packet-pair is sent back-to-back to the target which echoes them back to the sender. The spacing between the two packet shown in Figure 2 is determined by the bottleneck link and preserved by higher-bandwidth links[33]. Packets in packet pair arriving at the target node have a determined time separation between packets specified as ∆in . After interacting with the cross-traffic coming from different sources, the packets leave the output queue with changed time-separation stored as ∆out . Researchers inspired to formulate models based on variation between ∆in and ∆out . The published PPAPT differs in the way they increase the packet sequence rate and in the metrics measured on the PP flow. We classify the published PPAPT as SLPPAPT, PPTDAPT, RPPAPT and HPPAPT and describe various packet-pair techniques under each category in this section. Probing Traffic
in
P2
out
P1 P2
P1
Cross Traffic
Figure 2: Effect of Probing Packets at Node with Time-separation[33]
2.2.1
Self Loading Packet-Pair APT (SLPPAPT)
Trains of PP at various rates are probed into the network in iteration. Compare to non-iterative PPAPT, it requires more probing bits but yields more accurate estimation. Requirements of probing bits result in long measurement time and severe intrusiveness. It assumes a link is tight, but it does not presume knowledge of that link’s capacity. If the PP sending rate is faster than the available bandwidth, the PP will queue at some routers so that end-to-end delay increases gradually otherwise, the PP will experience little delay[25]. The delay variation at the receiver is used to either estimate the available bandwidth as the minimum probing rate that does not saturate the tight link or decide the time when congestion begins. It does not 10
suffer from the underestimation error because it does not rely on any explicit relation between the input rate, available bandwidth and output rate at the tight link[24]. The list of SLPPAPT protocols includes SLoPS[19][55], TOPP[2][56], PTP[25][15], SLDRT[31] and PAB[32] those are discussed in this section. SLPPAPT tools include PTR[11], TOPP[56], pathload[59], PathMon[51], Pathvar[52], pathChirp[53], YAZ[54] (calibrated available bandwidth tool) and Kalman Filters (KF) based available bandwidth tool[26]. A. Self-Loading Periodic Streams (SLoPS) Problem Statement: SLoPS[19][55] estimates the end-to-end path available bandwidth using sent PP of equal size and measured one way delay (OWD) of the PP. Methodology : SLoPS sends a packet-pair stream at specified rate and estimates the available bandwidth from the inference of self-induced congestion. A TCP connection is initially established between sender and receiver. Through this connection, the sender of PP includes the value of current ∆in to the receiver. The receiver calculates the ∆out and returns a message with the value of ∆out to the sender. The sender can determine whether the probe rate is beyond the available bandwidth after getting knowledge of ∆out and adjusts the range of probe rate. The probe rate is adjusted exponentially to demarcate rough bandwidth range and constrained to bandwidth value with quick search in the rough range. Contrary to traditional SLoPS techniques[55], the new SLoPS technique infers the congestion from a parameter named interval difference not end-to-end delays[19]. The probe rate can fast converge to the available bandwidth value in new SLoPS. SLoPS features includes fewer overheads than the other similar techniques, a fast and more accurate available bandwidth for short-term decisions, and no time-stamp requirement in the packets[55]. Results: If available bandwidth fluctuates greatly in short time, the measurement delay will increase, and the cost will rise. It cannot adjust its measurement granularity according to the load of network[19]. The OWD of a periodic stream show increasing trend if the stream rate is greater than the available bandwidth. Applications: SLoPS is used for tuning ssthresh parameter in TCP suite, overlay networks and end-system multi-cast, rate adaptation in streaming applications, end-to-end admission control, server selection with any-casting and verification of service level agreements. Available bandwidth estimated based on SLoPS has been incorporated in a web-based application for wired network [20]. B. Trains of Packet Pairs (TOPP) The differences between the SLoPS and TOPP techniques are related to the statistical processing of the measurements[2]. TOPP increases the offered rate linearly, while SLoPS uses a binary search to adjust it. An important difference between TOPP and SLoPS is that TOPP can also estimate the capacity of the tight link of the path. This capacity may be higher than the capacity of the path, if the narrow and tight links are different. SLoPS/TOPP can detect a change in the available bandwidth during the measurement. SLoPS/TOPP overloads the network path due to self-induced congestion.
11
SLoPS/TOPP assumes (1) First-in-first-out (FIFO) queuing at all routers along the path (2) the average rate of cross-traffic changes slowly and is constant for the duration of the measurement. Problem Statement: TOPP[2][56] estimate the available bandwidth of a network path by exploiting the time gap between PP using dispersion technique. Methodology : TOPP sends a train of packet pairs at gradually increasing rates in iteration from source node to the sink node. The rate is changed by modifying the input gap of each pair. The available bandwidth is estimated as the maximum input rate not larger than the measured rate at the destination. Suppose that a packet pair is sent from the source with initial dispersion Ds , the PP has a size of L bytes and thus the offered rate of the packet pair is R0 = L/Ds . If R0 is more than the end-to-end available bandwidth A, the second PP will be queued behind the first PP and the measured rate at the receiver will be Rm < R0 else TOPP assumes that the packet pair will arrive at the receiver with the same rate it had at the sender[2]. TOPP estimates hidden bottleneck. It has high efficiency with no self-interference. It is a network friendly. Accuracy is affected when the measurement errors propagated seriously using the theoretical model. Results : The set of experiments and simulations show that TOPP can handle single hops and multiple hops with varying bandwidths and cross-traffic. The number of congested links can be detected as long as their surplus bandwidths are all different. Applications : TOPP used to estimate available bandwidth of any path in communication networks particularly wired network. C. Packet Train Pair (PTP) Problem Statement : PTP[25][15] estimates both end-to-end available bandwidth and link capacity with a single measurement by focusing on estimation error and the amount of probing bits on the Internet. Methodology: PTP (smart probing technique) uses the basic concept of impulsive packet proving technique, which has a potential to address the problems of iterative probing like an increased amount of PP and a poor measurement accuracy. PTP uses a pair of probe trains with rates V1 and V2 , resulting in a small amount of probing bits. It includes a transmitter which generates probe trains each consisting of multiple packets. A receiver receives those probe trains, measure probe-train dispersion and calculates available bandwidth. Consider VL as link capacity, VC as capacity used by users, then the available bandwidth of link Vab is given by Vab = VL − VC . It assumes that all the PP has same length, and both packet trains
are composed of an identical number of PP, resulting in different train duration t1 and t2 . These packet trains are merged at the ingress router with cross-traffic from users and after that segregated at the egress router. The duration of the packet trains at the receiver are t′1 and t′2 that is changed due to the cross-traffic. The available bandwidth is calculated as given in Equation 1 and the link capacity is computed as given in Equation 2. t′
Vab =
t′
(( t11 − 1) × V2 ) − (( t22 − 1) × V1 ) t′1 t1
12
−
t′2 t2
(1)
VL =
(V1 − V2 ) t′1 t1
−
t′2 t2
(2)
To improve measurement accuracy, a sweep technique is used that updates the estimated value of available bandwidth. The technique makes full use of a limited set of measurement results with a few PP, yielding short measurement time and small amount of probing bits, while keeping measurement error minimum. It works fine with a single-hop network. The estimated link capacity with PTP in two-hop networks does not correspond to that of a narrow link, but to that of a tight link. Results: Conventional available bandwidth measurement techniques require a number of measurement cycles for estimating each point of available bandwidth, while the PTP requires only twice. The available bandwidth is estimated with relatively small error, while link capacity was estimated with relatively large error (i.e. around 10%) because link capacity was estimated as the sum of available bandwidth and cross-traffic load, both of which had estimation errors. Applications: PTP is used to measure available bandwidth for Internet applications in communication networks. D. Self-Loading Decreasing Rate Train (SLDRT) Problem Statement: SLDRT[31] measures the available bandwidth by using single decreasing rate packet trains under cross-traffic. Methodology: SLDRT sends single decreasing rate packet trains with PP. These packets accumulated at the tight link and then cause the OWD of successive packets at the receiver showing an increasing trend. With the rate of packet-train decreasing, congestion at the tight link eliminated gradually, and the OWD will show a decreasing trend. At last, the OWD remains approximately stable when the input rate of PP trains equals to the available bandwidth. SLDRT infers available bandwidth using the rate of whole PP trains instead of the rate of individual packets. In this way, the measurement bias caused by bursty traffic is efficiently eliminated. In the real network, the relative OWD is distorted by clock skew between the sender and receiver. SLDRT estimates the available bandwidth more quickly, accurately and with less measurement overhead than other existing techniques, but injects more PP in a relatively short time interval. Results: SLDRT is tested under fluid the cross-traffic model, under the non-fluid crosstraffic model for single hop and multi-hop scenarios. Robustness of SLDRT is tested for the amount of cross-traffic changes during the successive measurement, but the amount of crosstraffic remains constant during the measurement. Simulation results are explained to understand the role of the various SLDRT parameters like decreasing factor, initial rate factor and number of loading packets. Applications: This technique is more suitable for the multi-hop network applications. E. Probabilistic Available Bandwidth (PAB) Problem Statement: PAB[32] estimate probabilistic available bandwidth of multi-paths 13
in the network. Methodology: PAB determines the largest input rate at which traffic flow along a path can send achieving an output rate that almost as large as input rate with specified probability. PAB executes three tasks: (1) probe a path and produce a measurement outcome - probing strategy based on the principle of self-induced congestion and the probing rate is selected at every iteration, (2) compute the marginal posteriors of the path PAB from measurement outcomes by running belief propagation on the factor graph and establish confidence intervals for the PAB and (3) identify measurements (choose the path using weighted entropy/weighted confidence interval algorithm and probing rate) at each iteration that minimizes the overhead on the network. Average number of measurements and bytes per path required to complete the estimation procedure is a function of the probe train size. It provides a more valid mapping between the measured and inferred quantities. By expressing available bandwidth directly in terms of the input and output rates, there is no longer a need to bridge the gap between packet dispersion and utilization (or cross-traffic) through generally invalid modeling assumptions. The probabilistic framework gives flexibility to the user and is more resistant to variability (cross-traffic burstyness) and noise (errors) in the measurements. Lastly, it represents a more practical and concrete quantity in terms of the probability that transmits data at a given rate with the desired output rate. Results: Since the number of measurements is constant, the number of bytes required to achieve the desired accuracy is linear. From the results, it is clear that using 25 packets per probe train is optimal as it provides similar accuracy to larger probe train sizes with significant savings in terms of the number of probes. Applications: PAB used to measure the network-wide available bandwidth for applications such as overlay network routing, anomaly detection, SLA compliance, network management, transport protocols, traffic engineering and admission control. 2.2.2
Packet Pair/Train Dispersion (PPTD) APT
PPTD is also known as direct probing (as available bandwidth is estimated directly by measuring the gap between probes) or PGM (as available bandwidth inferred from a direct relation between the input and output gaps of measurement packet pairs with a single probing rate) technique. It is a lightweight and fast available bandwidth technique. The crossing traffic gets in between the PP and disperses them as shown in Figure 2. The available bandwidth is obtained by building the mathematical formula regarding the measured sending gaps and receiving gaps between PP. If the PP is sent at the rate of the bottleneck capacity C, the available bandwidth A can be calculated from the average dispersion rate R at the receiver as A = C(2 − C/R)[36][40]. Two back-to-back packets are sent on the network to estimate A thereby minimizing the chance that crossing traffic will disperse the packets. This
bottleneck delays the second packet with respect to the first packet. Bottleneck capacity C is calculated from the minimum dispersion D and the packet size L as C = L/D[36]. Two assumptions made are as follows: (1) the tight link is the same with the narrow link (bottleneck
14
link) and (2) the capacity C of the bottleneck is known in advance. To minimize the chance that crossing traffic disperses the packets further down the path after being dispersed by the bottleneck, a series of packet-pair probes is performed (packet pair techniques by using packet trains) and subsequently take the minimum of the dispersions observed. Its accuracy depends on the routing of cross-traffic relative to the measurement traffic. Even though it is accurate in the case of a single queue (as the cross-traffic follows the same path of the measurement traffic), it cannot estimate the available bandwidth of multi-hop paths, even if there is a single bottleneck in the path[24]. The list of PPTDAPT protocols includes IGI[11][7], TWABE[16], GNAPP[10], NLCPPD[72], bTrack[73] and AABE[13] those are discussed in this section. PPTDAPT tools include NetDyn probes[57], Delphi[58], Pathload[59] further examined in [60], bprobe[61], Pathrate[62], Packet Bunch Mode (PBM)[63], MoSeab[65], Treno[65], cprobe[61], SOProbe[66], Traceband[33], nettimer[67] and cap[68]. Performance evaluation results reveal that BE techniques and tools are far from being ready to be applied[28][54][71][69][70][34]. A. Initial Gap Increasing (IGI) Problem Statement: IGI[11][7] describes the available bandwidth on a path through the network by determining the input packet pair gap that gives a high correlation between changes in the packet gap and the competing traffic on the bottleneck link. Methodology: IGI sends a sequence of packet trains with increasing initial packet gap, captures the relationship between the competing traffic on the bottleneck link and the change in the gap between a packet pair probe in a single-hop network. The difference between the average output gap and the input gap for each train is monitored. The available bandwidth is obtained by subtracting the estimated competing traffic bandwidth from an estimate of the bottleneck link bandwidth. This single-hop gap model is used to understand the relationship between router queue size, competing traffic throughput, and input/output gap. The measurement errors are minimal so that it accurately estimates the amount of competing traffic on the bottleneck router in the single-hop gap model. However, the accuracy is low for light competing traffic in the multi-hop gap model because light competing traffic only increases a small number of probing gaps, and the timing measurement error becomes more significant. Results: The input gap plays an important role in measuring competing traffic, which has to be dynamically tuned to reach the point where the packet pair gap values correctly reflect the available bandwidth. In 90% of given experiments, the IGI completes in 6 probing phases and 80% competing traffic measurement cluster in a range smaller than 10% of the bottleneck link capacity. Applications: Regular Internet users do not have access to network internals, so IGI used to characterize the available bandwidth on a path through the network. B. Two-Way Available Bandwidth Estimation (TWABE) Problem Statement: TWABE[16] estimates the available bandwidth of both up-link and
15
down-link paths (with multiple tight links) using the concepts of traceroute and ICMP timestamp. Methodology: Traceroute is implemented by ICMP that is used to calculate the path length (in H hops) from the sender to the receiver. At the ith round (i = 1, 2, . . . , H), the sender sends N ICMP PP (a PP trains) with T T L = i to the receiver - totally N H PP sent in a whole probing process. All PP is of the same size, but the gaps between successive packets should be strictly increasing or decreasing for enhancing the estimation accuracy. When the sender receives the returned PP, the input and output gaps of any two consecutive PP are computed according to their timestamp, which is used to calculate capacity Cj and cross-traffic rate λj for tight link j. Then available bandwidth is Aj = Cj λj . TWABE also works in the environment with packet loss since any packet loss can be detected by checking the sequence number of every ICMP packet. Results: The estimation errors for tight links are less than 6%. Therefore, TWABE cannot only fulfil the two-way available bandwidth but also realize the available bandwidth inference for all tight links. The estimation accuracy improves significantly if the number of PP increases as the estimation error becomes smaller and can be less than 5% for all cross-traffic types at N = 300. Larger PP sizes result in better available bandwidth estimates. And a shorter cross-traffic packet length corresponds to a smaller estimation error. Applications: TWABE is used in the multimedia streaming applications adopting Scalable Video Coding (SVC). C. Available Bandwidth Analysis with Gaps of Non-Adjacent Probing Packets (GNAPP) Problem Statement: GNAPP[10] measures available bandwidth bidirectional similar to TWABE[16]. It evaluates the available bandwidths of several tight links in the up-link path or the down-link path. Methodology: Most of the probing techniques are not applicable to multimedia streaming networks as they evaluate available bandwidth of a path in only one direction, such as from the sender to the receiver. GNAPP uses Traceroute (implemented by ICMP to identify all intermediate nodes (routers) along a network path) and uses modified Ping program that employs ICMP Timestamp. When ICMP packet returns to the client at local time, the gap between any two PP is calculated by using the timestamp for available BE. The gap between certain two consecutive PP remains unchanged if the passed link is not congested; otherwise, it grows. Under the network with bursty cross-traffic, the available bandwidth accuracy degrades. To reduce the impact of two consecutive PP not capturing any cross-traffic on the estimation accuracy, the gaps of nonadjacent PP in addition to those of consecutive PP are adopted for analysis. Moreover, a moving average is utilized and a novel two-stage filtering algorithm is introduced to improve the estimation accuracy. The filtering algorithm reduces the probability of large errors, which might otherwise be caused by estimation noise. Results: Numerical results show that the filtering algorithm can significantly improve in-
16
ference accuracy. Reliability of GNAPP estimation technique under the Pareto ON-OFF crosstraffic is better than that under the Poisson cross-traffic. Applications: This two-way available bandwidth probing mechanism is used not only for general network measurements with bursty traffic but also for multimedia streaming applications. D. Estimating Network Link Characteristics using Packet-Pair Dispersion (NLCPPD) Problem Statement: NLCPPD[72] analyzes packet dispersion based bandwidth probing techniques on unicast paths and multicast trees by developing theoretical models of discrete-time queues considering link characteristics. Methodology: Packet-pair probes with predetermined separation between them are injected at a source. These PP traverses discrete-time queues on a path or on a multicast tree. For a single queue and for a given separation between the probes at the input, the conditional distribution of the separation between the probes at the output of the queue in terms of the distribution of the arrival process is derived. The key result here is the joint distribution of the number of arrivals to the queue and the number of departures from the queue between the slots in which the probes are injected. Using this, the distribution of the output separation is obtained for any given distribution of the input separation. For multicast trees, the joint distribution of the separations at all the outputs is obtained. A possible parameter estimator is the minimiser of a suitable distance function between the empirical distribution of the output separation and the theoretical distribution computed as a function of the parameters. Result:
The estimations work fairly well for two queues in series and for multicast.
Multicasting can significantly improve the estimates in terms of efficiency and accuracy, even when the depth of the tree is more than two. Applications: NLCPPD estimate network parameters on unicast paths and multicast trees in networks. E. Feedback-assisted Robust Estimation of Available Bandwidth (bTrack) Problem Statement: The bTrack[73] estimates the available bandwidth by exploiting the quasi-invariant characteristic of the relative distance obtained from input and output gaps of packet pairs. Methodology: Periodically, two pairs of PP of different sizes are sent and the relative distance for accurate estimation of the available bandwidth is exploited. The amount of the probing traffic is independent of the available bandwidth and is adjustable by fine-tuning the period of transmission of PP, which shows the non-intrusive nature. The bTrack utilizes the information on the packet dispersion as well as the PP size to exploit all the information available from the packet pair relation. It tracks the available bandwidth by exploiting the relative distance which is the difference between the characteristic values of two pairs of PP of different packet sizes. For the feedback mechanism of bTrack, the time axis is divided into intervals and in each interval, the sender transmits two pairs of PP to the receiver, with the inter-
17
departure times determined based on the results from the previous interval. After measuring the inter-arrival times of the two pairs of PP, the receiver estimates the available bandwidth on the end-to-end path and advises the sender of the inter-departure time to be used in the next interval. Results: It tracks available bandwidth quite well with a small variance (less than 8% of the value to be tracked) and is non-intrusive (i.e., the bandwidth consumed by the probing traffic is extremely low). Applications: It is used to prevent routers from overloading with traffic measurement and report tasks, in the QoS sensitive Internet services such as peer-to-peer file sharing applications and on-demand multimedia streaming applications and to improve performance of overlay networks. F. Adaptive Available Bandwidth Estimation (AABE) Problem Statement: AABE[13] estimate bandwidth by using active probing with packet pair dispersion with varying overhead. Methodology: AABE uses direct probing in each adaptation period of the video streamer by sending a single packet train with fixed gap size and fixed packet size. Since the video data is periodically sent and there is enough room in every period, the transmission of video data is not interrupted. Packet pair technique used for the estimation of bottleneck link capacity and IGI formula used for estimation of cross-traffic. The cross-traffic is subtracted from bottleneck link capacity to obtain an estimate of available bandwidth. Depending on the estimated available bandwidth value and PP loss rate, AABE adjusts the number of packets to be used in the packet train of the following period which alleviates the negative effects of active PP when there is congestion and improves accuracy when there is available bandwidth. Result:
AABE increases streaming performance substantially with higher perceptual
quality. Applications: AABE is used in Internet applications such as video streaming. 2.2.3
Reactive/Wireless Probing Technique (RPPAPT)
RPPAPT estimates the available bandwidth in wireless network. To activate reactive available bandwidth measurement, the source node sends a PP to the destination node. If no PP is received at the source node before timeout period (the PP sent may be lost), it sends the PP to the destination node again. When the destination node receives the PP sent by the source node, it replies by sending series of back to back PP on all paths to the source node periodically. The source node is now the receiver of the PP. These packets travel along paths from the sender node to the receiver node and produce gaps between them. These gaps are measured at the receiver node. The receiver node estimates the number of lost PP because each PP has a unique sequence number. All these raw gaps and the estimated number of lost PP feed into a filtering module which removes the biggest and the smallest gaps. Gap is calculated as the mean of the remaining gaps which is used to calculate available bandwidth. The list of RPPAPT protocols
18
includes BART[77][78], MR-BART[79], MiBT[29], DACME[80], RBM[43][81], NFE[82], DCSPT [14], and PPRCH [14] those are discussed in this section. RPPAPT tools include WBest[40], DietTOPP[74], AdhocProbe[75] and ProbeGap[76]. A. Bandwidth Available in Real-Time (BART) Problem Statement: BART[77][78] estimates an end-to-end available bandwidth quasicontinuously in real-time over a network path by using a KF. Methodology: BART relies on self-induced congestion. It repeatedly samples the available bandwidth of the network path with sequences of PP sent at randomized rates. It maintains a current estimate, which is incrementally improved with each new measurement of the interpacket time separations in a sequence of PP. If the probing sequence rate is less than the available bandwidth, new available bandwidth is not measured. These estimated values are given as inputs to the KF along with the suitable estimates of the process noise covariance Q and the measurement noise covariance R parameters that affect the Kalman gain K. The contribution for a new measurement is decided by these parameters when updating the systemstate estimate. The R is a scalar that describes the precision of the strain measurement. This parameter estimated based on the received packet pairs in a probe sequence. Q treated as an adjustable parameter describes the intrinsic fluctuations in the system related to the volatility of the cross-traffic. BART uses a linear model that relates the inter-packet strain (the time gap between two consecutive packets) with the probe traffic rate. Then the available bandwidth is computed as the point where the line intercepts the horizontal axis[78]. Computation overhand, memory overhead and probe traffic is low in every iteration. Results: Results given in [77] shows that the accuracy of BART is linear with probe-packet size and probe-train length. KF provides a promising engine for fast, efficient and reliable estimation of available bandwidth. The variability of the true available bandwidth dependent on the time resolution and does not depend on the estimation parameters. For a more finegrained time resolution, the variability is higher. Applications: BART is used in congestion control and for adaptation of transmission intensity in real-time applications (such as streaming of audio and video). B. Multi-Rate Available Bandwidth Estimation in Real-Time (MR-BART) Problem Statement: MR-BART[79] estimates the end- to-end available bandwidth of a network path from the transmitter to the receiver by employing multi-rate PP sequences with KF. This technique changes the probing rate from one probing sequence to another as well as alters the probing rate within each probing sequence. Methodology: A node of the network path consists of a queue connected to an input link and an output link. The queues in the nodes have infinite buffer length with FIFO as serving discipline. A network path consists of more than one link having its own capacity. The link capacity is determined based on the physical layer interfaces of the transmitter and the receiver. A cross-traffic with time-varying rate is observed on the link to describe the traffic fluctuations.
19
The available bandwidth of the link is computed as capacity minus traffic fluctuation rate. For a network path consisting of more links, the link with the minimum residual bandwidth link is available bandwidth (called the bottleneck link). If sending rate is smaller than the available bandwidth, the packets do not cause congestion on the network path with same the transmitting and receiving rates. Otherwise, the packets are backlogged, thus the congestion is experienced. MR-BART estimates the available bit-rate of a path when a sequence of the PP is injected into the path of interest. The inter-packet strain is obtained for each PP by measuring the inter-arrival time of the consequent packets at the receiver. For KF, linear equation used is : xk = f (xk−1 ) + wk−1 where xk is the state vector and wk−1 is the process noise at the kth step of the estimation. Using inter-packet strain and KF, the available bandwidth is calculated. Varying the probing rate in each probing sequence increases the variance of the strain of the sequence. This technique enables us to obtain a rich set of observations by injecting each probing sequence. The observed set of data is then utilized by a KF to estimate the available bit rate. Results: Mean-Square Error (MSE) is considered for available bandwidth error. MSE decreases with increase in simulation time. The dimension of measuring KF vector increases that yields more accurate estimation of available bandwidth. The computation complexity is reduced in a linear fashion. Applications: This technique is more suitable for congestion control and streaming of audio and video applications where an accurate estimation of the available bandwidth has a crucial role in providing QoS. C. Minimal Backlogging Techniques (MiBT) Problem Statement: MiBT[29] estimates the available bandwidth using the statistic of the probing traffic service rate. Methodology: This technique avoids PGM and PRM usage. The statistic of the probing traffic service rate is a consistent estimator of the available bandwidth for a G/G/1 queuing system under a minimal backlogging condition to support the MiBT theoretically. To emulate the MiBT in a real multi-hop network, the minimal backlogging condition or closeness of the probing rate to the available bandwidth based on the busy period length is detected. The probing rate is changed adaptively to maintain the minimal backlogging condition. The busy period of PP detects the more accurate minimal backlogging condition than the gap response curve or rate response curve. The initial probing rate estimation mechanism reduces the gap between the initial probing rate and the available bandwidth. A reasonable range of available bandwidth for a short time interval can be obtained using the mean and variance of the estimated available bandwidth. Results: MiBT has higher accuracy than other available bandwidth schemes, especially for highly dynamic sampled Internet traffic. Intrusiveness of MiBT is 0.012 lower than that of pathload for the 100-Mbps link and can be reduced further for a 1 Gbps link. Applications: MiBT is used for capacity provisioning, network troubleshooting, traffic engineering (TE), admission control and end-to-end QoS provisioning.
20
D. Distributed Admission Control for MANET Environments (DACME) Problem Statement: DACME[80] assesses available bandwidth in an end-to-end path, the end-to-end delay and jitter in Mobile Ad hoc NETworks (MANETs). Methodology: DACME uses different kinds of probes to assess the available bandwidth, the delay and the jitter on an end-to-end path in the network. The source assesses the available bandwidth by sending PP. The generated back-to-back PP is followed by a probe reply to the destination. At source, agent keeps a timer for detecting probe reply losses. The destination, upon receiving all the PP, is expected to send a single reply packet with the measured value for the available end-to-end bandwidth as given in Equation 3 where Sd is the size of the data segment of each packet, tlpr is the time at which the last packet received, tf pr is the time at which the first packet received and Pr is number of packets received. Repetition of this process achieves more accurate results. ABW =
8 × Sd × (Pr − 1) tlpr − tf pr
(3)
Results: DACME offers a new framework for QoS support in MANETs based on Media Access Control (MAC). The probe sets sized 5 is a reasonable choice which offers a good balance between available bandwidth accuracy and admission control time. Applications: DACME is used for real-time services in MANETs based on distributed admission control. E. Reactive Bandwidth Measurement in 802.11 Networks (RBM) Problem Statement: RBM[43][81] derives the available bandwidth re-actively from the measured gap between the two PP at the destination node over 802.11x network. Methodology: RBM is the reactive bandwidth measurement in which node sends the PP to the destination to activate the measurement process and the destination sends series of PP to the source in a regular interval. To send the first successful PP, the sender needs to send the RTS to the next hop node, after that the CTS, DATA and ACK packet follows. The second successfully sent PP may follow the first PP immediately but with delay tothernode between these two PP when the medium is used by other nodes or there’s a contention in the medium. Total time to transmit a PP from the sender node to the next hop node consists of the time to transmit RTS (trts ), CTS (tcts ), the PP itself (tdata ), the ACK (tack ) for the PP and the processing time. So the gap between the first and the second PP in the next hop node is: GAP = tack + tothernode + trts + tcts + tdata + tprocessingtime . In the best-case scenario, tothernode is zero so GAP = GAPbest . If the next hop node is not the destination of the PP, the next hop node forwards these packets again until they reach the destination. The tothernode is accumulated during this process and measured in the destination node by a gap between the two PP. This gap is called the Gapmeasured . If B is the currently maximum available bandwidth, the available bandwidth along the path is calculated as: ABW = (GAPbest /Gapmeasured ) × B.
Results: Two sets are used to evaluate the correctness of the available bandwidth measure21
ment. The histogram of reactive available bandwidth measurement in one hop with 60pkts/s CBR connection shows that the remaining available bandwidth is 83KB/s on the average. The histogram of reactive available bandwidth measurement in two hops with no CBR connection shows the remaining available bandwidth is 62.5KB/s, less than one hop scenario. Applications: RBM can be used in a network of multiple mobile equipments in a complex hybrid network and for load balancing in MANET. F. Neuro-Fuzzy Estimator (NFE) Problem Statement: NFE[82] estimates accurate functional forms of the end-to-end available bandwidth between a source and a destination using a dispersion trace of PP in the neurofuzzy system over an IEEE 802.11b ad hoc network. Methodology: The gaps between PP that suffer the minimum sum of OWD are used to estimate the maximum achievable transmission rate (or bandwidth) as a function of variable size of packet lengths. The variability of the dispersions is used to estimate the fraction of that bandwidth as a function of packet length that is effectively available for data transmission. Available bandwidth differs from the unused capacity. As the unused capacity is occupied, the arrival of the new flow re-accommodates the occupation pattern in the neighborhood of its path. Available bandwidth is further reduced by cross-traffic, which (1) simply decrease the signal-to-noise ratio at some parts of the path, or (2) interacts through MAC arbitration at some other parts of the path, or (3) even share some common queues along the path. These are some of the ignored aspects that are indirectly captured by the neuro-fuzzy system. In order to consider all these aspects, the NFE is trained on larger data collected. With all these data, the estimator learns how to infer the available bandwidth from the variability of the dispersion traces. Result:
The high accuracy of the estimation and the detailed available bandwidth trace
resolution is achieved by advancing 320-packet analysis windows for every 8-packets under no losses for 40 seconds. Applications: NFE is used in applications such as resource allocation, data rate adjustment, traffic engineering, resource-constrained routing, capacity planning, peer-to-peer file sharing, etc. G. Dual-Carrier Sense with Parallel Transmission-awareness (DCSPT) Problem Statement: DCSPT[14] estimates the available bandwidth by considering interference from carrier sensing neighbors without being overly conservative (i.e. parallel-transmission aware). Methodology: Wireless mesh node surrounding transmissions can be changed by adjusting its Carrier Sensing (CS) threshold. These adjustable CS-thresholds are overly conservative; they under-estimate available bandwidth because they do not account for several transmissions occurring in parallel due to spatial reuse. This reuse is typically observed between the transmissions of the same flow, as well as between the transmissions of different flows crossing
22
the network. Wireless mesh node samples local channel busy time by passively monitoring local transmission activities busy at the level of the regular CS-threshold. By switching to the lower CS-threshold, a node observes its extended surrounding area to measure its CS-neighbors channel busy time. Using these measurements, a node calculates the percentage of channel utilization due to the transmissions that originate from the nodes outside its regular CS-range ρlocal and the channel utilization due to transmissions at greater distance ρcsn . The channel idle fraction for the purposes of available bandwidth is 1 − ρcsn . DCSPT cannot be implemented
without explicit support from the vendor.
Result: Through parallel transmission awareness, cumulative system throughput improvement up to 80% is observed. Applications: DCSPT is used in IEEE 802.11-based wireless mesh network applications such as streaming of audio and video content from entertainment and surveillance devices. H. Packet Probing with RTS/CTS Handshake (PPRCH) Problem Statement: PPRCH [14] estimates the available bandwidth by changing the software of wireless mesh networking equipment for measuring probe dispersion using RTS/CTS Handshake. Methodology: CS-threshold adjustment capabilities are not readily offered by wireless transceiver vendors. PPRCH measures the probe dispersion by taking the time difference between the completion times of two subsequent PP transmissions using RTS/CTS. PPRCH considers the advantage of IEEE 802.11 virtual carrier sense in capturing activities due to the hidden nodes around measuring station. PPRCH has a two-module system within Linux kernel: probe generator and gap adjuster. The probe generator module is responsible for generating the two PP; each transmitted from a different IEEE 802.11e priority queue. This ensures that the first PP is prioritized to mark the start of the available bandwidth measurement as soon as possible, and the second probe is sent out with a lower priority, favoring other mesh nodes to transmit their packets (i.e., because of the reduced probability of collision with the second probe). The second probe is always delayed by the amount dictated by the gap adjuster module. Available bandwidth is measured at a mesh node by repeating the probes with re-adjusted gaps based on the observations for the dispersion of the previous probe. Result:
PPRCH (interference-aware approach) performs robustly with less than 8.3%
MSE for the same two topologies given a conventional decodable-packets based on the BE strategy. Applications: PPRCH is used in IEEE 802.11-based wireless mesh network applications such as streaming of audio and video content for entertainment and surveillance devices. 2.2.4
Hybrid PPAPT (HPPAPT)
The information of available bandwidth on an end-to-end path is important for various network applications. Several probing techniques have been published to estimate the same. These techniques are either based on the fluid model or only partially suitable for bursty real Internet
23
cross-traffic. The accuracy of their estimation degrades at different extents in multi-hop situations. Also, all previous PGM based methods require the knowledge of bottleneck link capacity, which may not be available in practice. In this section, we discuss HPPAPT protocol known as PATHCOS++[27] that integrates the advantages PRM and PGM. A. PATHCOS++ Problem Statement: PATHCOS++[27] estimates the available bandwidth along a path by sending a train of time stamped PP from sender to receiver by integrating the advantages of both PRM and PGM based techniques. Methodology: PATHCOS++ is hybrid technique comprising self-induced congestion mechanism and packet gaps to estimate the available bandwidth. The receiver monitors the changes in OWD of the PP and conduct analysis based on the mechanisms. PATHCOS++ extends the analysis of queuing behavior of PP from single-hop scenario to multi-hop scenario. PATHCOS++ sends PP with rates controlled by a cos function and find big bumps in the probing response curves to conduct the available bandwidth without the information about the bottleneck link capacity. PATHCOS++ does not make fluid cross-traffic assumption. Results: PATHCOS++ is quite efficient that provides end-to-end available bandwidth with significant accuracy than current state-of-the-art techniques. The accuracy of PATHCOS++ is nearly unaffected when there are multiple congested links. Applications: PATHCOS++ is useful for various applications, such as traffic engineering, admission control, TCP start up performance improvement, adaptive streaming, multi-path transmission, optimal routing in overlay networks.
2.3
Comparison of APT Protocols
In this section, we provide the summary of various available bandwidth techniques under APT in Table 2. In the table, the meaning of the various columns, left to right, are as follows: available bandwidth technique - category and acronym to the proposing paper; where the BE done - at link or path; advantages and limitations in terms of accuracy, time taken for estimation of bandwidth, and overhead on the networks; Number of probe packets sent on the network; and Innovations the feature of the protocol that was not seen in proposals published before it.
3
Passive Techniques (PT)
PT is called as calculation based techniques in [39] that do not inject PP for calculating the bandwidth. The dispersion and delay are observed for the data flow and ACK packets without introducing any additional PP. PT works on network traces collected earlier. The communication is sensed to collect the network traces so called as sensing based mechanisms. The local information on utilization of bandwidth is used to calculate the available bandwidth which is exchanged via local broadcast. In this section, we discuss passive available BE techniques that are divided into two categories: GPT and PPT. MRTG system is PT tool[83].
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Table 2: Comparison of APT Protocols BE at
Accuracy
BE Time
Overhead Number of PP
Innovation
Medium
High
High
Multiple probe packet
Medium
High
Varies
RTT and packet size used to estimate bandwidth OWD used to adjust probing rate
√
Medium
High
Varies
√
High
Medium Low
Multi-hop path
High
High
High
Medium Varies
IGI
estimate probabilistic ABW on multi-path √
Packet pair probe stream at specified rate Many packet pairs at gradually increasing rates Pair of probe trains with different probing rates as V1 and V2 Single decreasing rate packet trains Packet pair probe stream at specified rate
√
Medium
High
TWABE
√
√
Varies
Medium Medium
√
High
High
Techniques
PPTDAPT(Direct)
PPAPT(Iterative)
SPAPT VPS SLoPS
Path √ √
For TCP
TOPP PTP
√
SLDRT
√
PAB
√
GNAPP
Link
paths with multiple tight links √ paths with multiple tight links
High
Varies
High
Sequence of packet trains with increasing initial packet gap Varies with number of hops in the path Varies with number of hops in the path
NLCPPD
√
√
High
Medium Varies
Packet-pair probes with predetermined separation
bTrack
√
√
Varies
Varies
√
Varies
Medium Low
Varies
Low
Low
Two pairs of PP of different sizes are sent periodically Packet pair with varying overhead Self-loading sequences of PP at randomized rates Multi-rate PP sequences
Unicast and Multicast path
AABE
bottleneck link
None
√
MR-BART
√
√
High
Low
Low
MiBT
√
√
Higher
Low
Low
DACME
√
Varies
Medium Medium
RBM
√
√
Medium
Medium Medium
NFE
√
√
High
Low
Low
Sender sends the PP and destination sends series of PP Series of PP
Hybrid PATHCOS++
√
High
Low
Low
Train of time stamped pp
RAPT
BART
3.1
Probing rate varies with minimal backlogging condition Different kinds of probes
ABW is the maximum offered rate Path ABW and link ABW computed at a time using time dispersion and probing rates Stability of OWD used to estimate ABW Weighted entropy/weighted confidence interval algorithm used on multi-path with specified probing rate Packet pair gap gives a high correlation between changes in the packet gap and the competing traffic on the bottleneck link Works in uplink, downlink and in the environment with packet loss Works in uplink, downlink and has novel two-stage filtering algorithm used to improve the estimation accuracy Conditional distribution of the separation between the probes at the output of the queue is considered Uses quasi-invariant characteristic of the relative distance Transmission of video data is not interrupted Estimation is quasi-continuously in realtime over a network path by using a KF Inter-packet strain and KF used for estimation Statistic of the probing traffic service rate is used Assess available bandwidth in an end-toend path, the end-to-end delay and jitter in MANET Designed for 802.11x network Designed for IEEE 802.11b ad hoc network considering signal-to-noise ratio, MAC arbitration, and sharing of queue Comprising self-induced congestion mechanism and packet gaps
Generic Passive Techniques (GPT)
GPT requires Probability Distribution Function (PDF) of packet inter-arrival in a TCP flow[12]. The PDF shows behavior of spike, spike bump, spike train and train of spike bumps. The characteristic behaviors are interpreted as a bottleneck with no substantial cross-traffic, a low bandwidth bottleneck followed by a high-bandwidth bottleneck, the traversed bottleneck shared with a substantial amount of cross-traffic and a low bandwidth upstream bottleneck shared with a substantial amount of cross-traffic. Clustering problem in this technique detects the shared bottleneck. In the receiving part of the end-to-end system, there is an observer who watches the arrival of packets at some link. After completion of all the steps, minimization of Renyi Entropy (which is a generalized version of Shannon Entropy formula) is used for discriminating between bottleneck sharing and non-sharing flows. In this section, we discuss GPT protocols such as EABRRL[30], TCPV[84], and TCPW[85].
25
3.1.1
Estimation of Available Bandwidth Ratio of a Remote Link or Path Segments (EABRRL)
Problem Statement: EABRRL[30] estimates the available bandwidth ratio of a remote link or remote path segments, a group of consecutive links, without deploying it at the remote nodes. Methodology: EABRRL estimates the delay distribution for a path segment from the monitoring node to another remote node. Two streams of ICMP timestamp packets are sent to both end nodes of a target link according to a Poisson process. It measures the OWD from the difference of the packet sending time and the timestamp value received from the remote nodes, extract the queuing delay component from the measured delay and estimate the product of the available bandwidth ratios of the links on a given path segment. Then, the available bandwidth is inferred from the ratio of the available bandwidth ratio products. It does not require any condition on the ratio of link rates of consecutive links. It estimates the available bandwidth ratio of the links beyond the tight link on a given path without overloading any network link. The intrusiveness in this protocol is as low as it does not incur short-term congestion. It overcomes the limitation of conventional approaches like inability to probe the links beyond the tight link with the minimum available bandwidth. Results: The measured value of the available bandwidth ratio lies within the standard deviation from the average of the estimation values in most links. Although, the variance increases when far links are probed, the standard deviation is still very small compared with the average. Applications: EABRRL is used for finding network anomalies caused by link/node failure or congestion, which is either an actual physical problem or malicious attacks such as DDoS attacks and worms that further deteriorate the QoS in the Internet. 3.1.2
TCP Vegas (TCPV)
Problem Statement: TCPV[84] estimates bandwidths as an active throughout during the connection time on the Internet. Methodology: TCPV computes the difference between the expected flow rate (cwnd/RT Tmin ) and the actual flow rate (cwnd/RT T ), where RT Tmin is the minimum RTT measured by the TCP source and cwnd is the congestion window size. TCPV adjusts the congestion window size based on the observation. When the network is not congested, the actual flow rate is close to the expected one. When the network is congested, the actual rate is smaller than the expected flow rate. Results: TCPV achieves 37% to 71% better throughput. In homogeneous scenarios, it fails to achieve fairness since competing connections can converge to different cwnd parameter values. Applications: TCPV has been introduced into the Internet.
26
3.1.3
TCP Westwood (TCPW)
Problem Statement: TCPW[85] estimates the available bandwidth by measuring the rate of acknowledgments. Methodology: The source node along the TCP path estimates the bandwidth measuring and averaging the rate of returning ACKs. It considers the sequence of bandwidth samples sampleBW E [k] obtained using the ACK arrivals from which a smoothed value BW E[k] is evaluated by low-pass filtering on the sequence of samples. BE (via ACK monitoring) is used before to control the TCP window, but only indirectly, via the estimation of the bottleneck backlog. After a congestion episode (i.e., the source receives three duplicate ACKs or a timeout), the source uses the measured bandwidth to set the congestion window properly, set the slow start threshold, and start a faster recovery procedure. When an ACK is received by the source, it conveys the information that an amount of data corresponding to a specific transmitted packet is delivered to the destination. If the transmission process is not affected by losses, simply averaging the delivered data count over time yields a fair estimation of the bandwidth currently used by the source. When duplicate ACKs (DUPACKs) reach the source indicating an out-ofsequence reception, they are also counted toward the bandwidth estimate and a new estimate is computed right after their reception. Results: TCPW performance is not very sensitive to random errors. TCPW is extremely effective in mixed wired and wireless networks where throughput improvements of up to 550% are observed. Applications: Bandwidth estimated in TCPW is used to control the congestion window and the slow start threshold directly.
3.2
Proactive PT (PPT) for Wireless Network
The wireless medium is passively monitored over a certain period of time. The available bandwidth is estimated using the measured channel-usage ratio without any impact on the existing flows. Physical (PHY) overhead is added during the transmission and it cannot be ignored in the available bandwidth. Hello packets of many routing protocols that discover the local topology are used to perform local broadcasts. PPT is considered as non-intrusive as Hello packets exchanges are not too frequent. PPT considers only the MAC overhead when calculating the available bandwidth. This available bandwidth is used for network selection in heterogeneous environment[86]. In this section, we discuss PPT protocols for wireless networks such as QoS-AODV[87], BRuIT[88], CACP[89], AAC[90], ABE[9], IAB[39], cPEAB[18], APBE[17], ABEWN[91], DABE[92][93], QBEM[37][94], DBE[95], HSBE[96], ABE11WN[97] and BER[98]. 3.2.1
QoS Enabled Routing in MANETs (QoS-AODV)
Problem Statement: QoS-AODV[87] calculates a per-node available bandwidth using the ratio between the numbers of transmitted and received packets. Methodology: QoS-AODV makes the resource consumption more efficiently by minimiz-
27
ing the unnecessary signalingand stopping the sessions that cannot meet the demanded QoS requirement. The evaluation mechanism constantly updates a value called Bandwidth Efficiency Ratio (BWER), which is the ratio between the numbers of transmitted and received packets. The available bandwidth is simply obtained by multiplying the BWER value by the channel capacity. This ratio is broad-casted among the one-hop neighbors of each node through Hello messages. The bandwidth available to a node is then inferred from these values as the minimum of the available bandwidths over a closed single-hop neighborhood. QoS-AODV considers not only the possibility to send a given amount of data but also the effect of the emissions of a node on its neighborhood[9]. Results: Usage of Hello message concept adds 20% more overhead for a traffic rate higher than 600 Kbps. Hello messages are sent every second for whatever traffic rate. Therefore, the proportion of the Hello message overhead decreases as the rate increases. Applications: QoS-AODV is used to provide QoS support in applications with guarantees in terms of bandwidth. 3.2.2
Bandwidth Reservation under InTerferences Influence (BRuIT)
Problem Statement: BRuIT[88] reserves the bandwidth for ad hoc networks that takes into account the existence of the interferences. Methodology: BRuIT takes into consideration the fact that the carrier sense range is twice the transmission range. Even if two nodes are not able to communicate with each other, they still contend the resources of each other in the ad-hoc network. To address this issue information related to bandwidth is shared with all its neighbors. BRuIT assumes that each node has a whole knowledge of the network. Even though, it is distributed protocol, it does not require control entity. Each mobile node periodically determines a set of mobile nodes that can interfere with it and their respective bandwidth reservation by flooding the network with a route request containing the amount of bandwidth desired in Hello packet. Admission control is made at each node that receives the request, and nodes do not forward it if they do not enough bandwidth available. Signaling overhead depends on the frequency of the broadcasts, but it also depends on the size of the Hello packets. The denser the network is, the larger the Hello packets will be. Maximum bandwidth between two nodes depends on the number of neighbors of per node. Filtering the flows has the positive influence on delays when congestion appears. Results: Maximum rate of 1.5 Mb/s between the two nodes is achieved (the theoretical link bandwidth of the modeled interface cards of 2 Mb/s). When sending ten Hello packets per second, the maximum bandwidth is decreased by about 20 kb/s which represents 1.3% of the total bandwidth at the application level. Applications: BRuIT is used in multi-hop network applications and ad-hoc networks where the whole knowledge of interference is taken into account.
28
3.2.3
Contention-Aware Admission Control Protocol (CACP)
Problem Statement: CACP[89] provides an effective, scalable admission control protocol for wireless ad hoc networks to maintain the end-to-end connections with QoS requirements. Methodology: CACP assumes that each node first computes its local proportion of idle channel time by monitoring the radio medium available. CACP consists of four parts: route discovery, admission control, building carrier-sensing range (C-neighborhood) sets and mobility management. C-neighborhood available bandwidth is the maximum amount of bandwidth that a node can use for transmitting without depriving the reserved bandwidth of any existing flows in its C-neighborhood. Local available bandwidth is the amount of unconsumed bandwidth as observed by a given node. Three different techniques are used to obtain bandwidth information at c-neighbors. First, the information is included in Hello messages to reach the two-hop neighborhood similar to BRuIT. Second, the nodes transmission power is increased. However, this emission power is often limited by regulations. Therefore, this technique may only be applicable when power control is used for regular transmissions. Finally, receiving nodes also reduce their sensitivity in order to decode the information coming from farther away, which depends on the quality of electronics and on the signal modulation[9]. Each time a control message is sent at an enhanced power level or in multi-hop mode, it causes more interference to the network than it has been sent as a normal message. Since a node can consume the bandwidth of nodes that are in its c-neighborhood, the c-neighborhood available bandwidth for a given node is equal to the smallest local available bandwidth of all of its c-neighbors. As a result, a node must have enough local and c-neighborhood available bandwidth to successfully admit a flow. Results: CACP enables a better QoS guarantee by limiting flows in the networks. It requires high overhead since a packet with the high power significantly affects the ongoing transmission. Simulations show that by controlling bandwidth allocation, delay and jitter can also be controlled. Applications: CACP is used to provide QoS support in applications with guarantees in terms of bandwidth, delay, or jitter. 3.2.4
Adaptive Admission Control (AAC)
Problem Statement: AAC[90] estimates end-to-end available bandwidth efficiently, which is used for adaptive admission procedure based on a cross-layer QoS routing. Methodology: Each node considers the set of potential contenders as a single node. It measures the activity period durations that is considered as a frame emission of the corresponding length. Collisions and distant emissions are also considered when computing the medium occupancy. Based on this measurement, each node can evaluate its available bandwidth. It exchanges this information with its neighbors to compute the bandwidth on each link as: ABW = (1 − K) × min((Ts /T ), (Tr /T )) × C where K = (DIF S + backof f )/T is the
proportion of bandwidth consumed by the waiting and backoff process, Ts (respectively Tr ) is channel idle time sensed by the sender (respectively receiver) in a measurement period T and 29
C is maximum capacity of the channel. AAC also takes into account the intra-flow contention problem. AAC assumes that channel idle period between the sender and the receiver is totally overlapped. Results: AAC takes into account the intra-flow contention problem. The assumption of AAC overestimates the real available bandwidth. Applications: AAC used for radio resource management and QoS provision in MANETs 3.2.5
Available Bandwidth Estimation (ABE)
Problem Statement: ABE[9] calculates the available bandwidth by collecting/exchanging information during the communication among neighbor nodes without any impact on the other existing flows. Methodology: ABE considers idle-period synchronization, the collision probability and the bandwidth consumption of the backoff mechanism. The idle time is defined as the total time in a given measurement period during which a node neither emits a frame, nor senses that the medium is busy. The estimated collision probability of Hello packets of routing protocol is used to calculate the overall collision probability under the assumption that the medium occupancy by the sender and receiver is independent of each other. The available bandwidth is calculated as: ABW = (1 − K) × (1 − Pc ) × min((Ts /T ), (Tr /T )) × C where Pc is packet collision probability, K is the proportion of bandwidth consumed by the waiting and backoff
process, Ts (respectively Tr ) is channel idle time sensed by the sender (respectively receiver) in a measurement period T and C is maximum capacity of the channel. Results: Throughput higher than 95% is observed by admitting the proportion of flows in the set of simulations with random number of nodes, location of these nodes, flow sources, flow destinations and flow throughput. ABE does not consider the control messages which may lead to inaccurate available bandwidth estimation. Applications: ABE is used in applications that generate multimedia data flows. 3.2.6
Improved Available Bandwidth (IAB)
Problem Statement: IAB[39] estimates the available bandwidth on a given link for QoS support in IEEE 802.11-based ad hoc networks. Methodology: IAB considers synchronization between sender and receiver by differentiatingthe channel busy caused by transmitting or receiving from that caused by carrier sensing. IAB improves the accuracy of estimating the overlap probability of two adjacent nodes idle time. A node is BUSY when it is in the state of transmitting or receiving and SENSE BUSY when it is in the state of sensing. Any other time the node is IDLE. The differentiation of these two time periods has an impact on the synchronization of the sender and the receiver and consequently, on the estimation of the overlap idle time and estimation of the available bandwidth. Results: Results of IAB performance evaluation for available bandwidth against different existing flow bandwidths as well as available bandwidth error statistics clear that IAB can 30
accurately estimate the existing bandwidth on a given link. Applications: IAB is used in bandwidth-constrained applications before performing QoS aware functions such as QoS routing, admission control, and flow management. 3.2.7
Cognitive Passive Estimation of Available Bandwidth (cPEAB)
Problem Statement: The cPEAB[18] estimates the bandwidth in overlapped WiFi WLANs environments. Methodology: The cPEAB considers the additional overheads caused by ACK frames, which are not considered in AAC and AAB. It estimates the available bandwidth by correct measurement of i) the proportion of Distributed coordination function Inter-Frame Space (DIFS) and backoff, ii) packet collision probability, iii) acknowledgment delay and iv) channel idle time in the measurement period. Hidden and exposed nodes are also considered in cPEAB for more accurate available bandwidth. In IEEE 802.11 Distributed Coordination Function (DCF), the frame exchange sequence of basic access mode is Data-Ack after the time taken by DIFS and back-off. A control message overhead also includes ACK delay. It does not consider the possible overhead caused by the exchange of RTS and CTS messages which, may lead to inaccurate estimation of available bandwidth as: ABW = (1 − K) × (1 − ACK) × (1 − Pc ) × (Ti /T ) × C
where K is the proportion of bandwidth consumed by the waiting and backoff process, Pc is the packet collision probability from hidden/exposed nodes, Ti is the channel idle time of the wireless medium in a measurement period T and C is the maximum capacity of the channel. Results: If the measurement period is short, any change in the network is reflected faster with lots of fluctuations but, more stable results are obtained with a longer measurement period. Even though, the proportion taken by the ACK procedure is not great, they still have the impact on estimating available bandwidth. Applications: It is used to estimate correct bandwidth for overlapped wireless access networks, such as WiFi, WiMax and UMTS. 3.2.8
Accurate Passive Bandwidth Estimation (APBE)
Problem Statement: APBE[17] estimates the bandwidth with RTS and CTS overheads. Methodology: APBE performs available bandwidth with the correct measurement of i) the proportion of DIFS and backoff, ii) packet collision probability, iii) acknowledgment delay and iv) channel idle time in the measurement period. It additionally considers the overhead caused by the RTS and CTS, which is calculated as: R/Cohd = ((RT S+CT S)+(2×SIF S))/T if RTS/CTS is used otherwise it is considered as 0 where SIFS is Short Inter-frame Space. The available bandwidth is estimated as: ABW = (1 − K) × (1 − R/C) × (1 − ACK) × (1 − Pc ) × (Ti /T ) × C
where K is the proportion of bandwidth consumed by the waiting and backoff process, Pc is the packet collision probability from hidden and exposed nodes, Ti is the channel idle time of the wireless medium in a measurement period T and C is maximum capacity of the channel. Results: The accuracy of APBE is higher and estimated error ratio of APBE is less compared to cPEAB. 31
Applications: APBE is used in the wireless networks for efficient network resource management, QoS guarantees of multimedia applications and effective admission control. 3.2.9
Agent based Bandwidth Estimation in Wireless Networks (ABEWN)
Problem Statement ABEWN[91] finds an optimal available bandwidth for connectionless and connection-oriented service using both the static and mobile intelligent agent. Methodology: ABEWN keeps an available bandwidth agency at every node. It comprises of a Bandwidth Manager Agent (BMA), Link Monitoring Agent (LMA), Path Discovery Agent (PDA) and a blackboard for inter-agent communication. BMA initiates available bandwidth operation that creates the black board to store knowledge information related to available bandwidth on overlapped wireless networks in 4G. LMA is created by BMA during the handoff call related to connectionless service and represented by the cost function. PDA is created by BMA during the handoff calls related to connection-oriented service. The cloned PDA for each link on the path traverses the path to destination and determines the minimum available bandwidth on the link of the path as well as the available bandwidth of a path made in 4G overlapped wireless networks. BMA updates the knowledge base for each link. The network with maximum bandwidth is selected as a target network for handoff call. Result:
It is claimed that ABEWN improves the conventional available bandwidth algo-
rithm in terms of measurement of latency and accuracy. Applications: ABEWN is applicable in the handoff decision process of 4G wireless networks. 3.2.10
Distributed Available Bandwidth Estimation (DABE)
Problem Statement: DABE[92][93] estimates the available bandwidth in distributed manner using the methods of channel monitoring, collision estimation and backoff duration prediction. Methodology: The link available bandwidth is influenced mainly by several factors, such as the channel utilization, the collision probability, the frame retransmission count and the backoff duration. The channel monitoring scheme is used to evaluate the channel utilization. Node obtains the collision status based on the current size of contention windows for each Access Category (AC) and predicts total frames to be transmitted in the next monitoring period. Backoff duration recorded locally and measured by each node with different ACs between the periods that the counting down of the contention window pauses and continues. When nodes need to estimate bandwidth, they collect the Network Allocation Vector (NAV) information and backoff duration from their neighbor nodes to compute the total busy period for the channel within the monitoring period. The busy period is comprised with frames transmission time, frame intervals and backoff durations. The link available bandwidth B between node source (S) and destination (D) is obtained as B = C − (C/TM ) ×
P
(SBusy [i] + DBusy [i]) where C is data
rate on the link, SBusy [i], and DBusy [i] are busy times of node S and node D for each AC and TM monitoring period.
32
Result: The error ratios of the estimated results and the measured results for Chain, Regular and Random scenario is 3.74, 4.575 and 6.895 respectively and when node density below 8%, it is 3.74, 3.04 and 4.973 respectively. Applications: DABE is used in MANETs application to provide QoS. 3.2.11
QoS-Aware Bandwidth Estimation for MANETs (QBEM)
Problem Statement: QBEM[94][37] calculates the residual bandwidth of the IEEE 802.11 MAC where the bandwidth is shared among neighboring hosts, and an individual host has no knowledge about another neighboring host traffic status. Methodology: QBEM has two methods to estimate bandwidth[94][37] which are listen available bandwidth and Hello available bandwidth. In listen available bandwidth methods, the host listens to the channel and estimates the available bandwidth based on the ratio of free and busy times. In Hello available bandwidth method, every host disseminates information about the bandwidth it is currently using in the Hello messages and estimates its available bandwidth based on the bandwidth consumption indicated in the Hello messages from its twohop neighbors. QoS-aware routing protocol uses the residual bandwidth in the request packet send by source to indicate whether to continue transfer of data or not. Mixed method of Listen and Hello is proposed in [37] where Listen available bandwidth method is used to estimate the bandwidth from the used bandwidth. Update scheme of Hello available bandwidth method is used during the route break to release the bandwidth immediately. Then it replies the request back to sender who sent the request according to the Listen available bandwidth method, which does not add an extra overhead. Hello method adds overhead by attaching neighbors bandwidth consumption information in the Hello messages. Result:
The Hello method performance is better than the Listen method in terms of
end-to-end throughput, while the Listen method performance is better than the Hello method in terms of the packet delivery ratio in mobile topology. Applications: QBEM is used for real-time video and audio applications. 3.2.12
Dual Bandwidth Estimation (DBE)
Problem Statement: DBE[95] similar to[94][37] estimates the bandwidth according to channel conditions using both the busy-time and the Hello method. Methodology: DBE switches between listening method and Hello method according to variations in the channel state. In busy-time method, the MAC layer detects that the channel is free or busy. Using busy and idle time, network utilization (U) can be calculated, which is used to calculate available bandwidth as ABW = (1 − U ) × Bmax where Bmax is maximum
possible bandwidth or network capacity. Hello messages are used to update the neighbor caches of AODV routing protocol in Hello method[94]. These messages keep the address of the host who initiates the message. If this Hello message is modified, it is piggy-backed with information containing the bandwidth consumed by each host and the bandwidth consumed by its neighbors. The available bandwidth is the raw channel bandwidth minus the overall consumed bandwidth. 33
When the route is not broken, the busy-time method is used to estimate bandwidth. When a route breaks, it switches to the Hello message. Results: As the mobility increased, the use of the listening method decreased and the use of Hello message increased. Applications: DBE is used in streaming applications such as videoconferencing, dynamic server selection and congestion control transports. 3.2.13
Highly Scalable Bandwidth Estimation (HSBE)
Problem Statement: HSBE[96] accurately estimates the current backhaul bandwidth of different access points in an efficient manner without requiring any installation of special software on the access points and not burdening the WiFi subscribers to perform any communication or computation intensive task. Methodology: HSBE is based on TCP protocol behavior. In general, when a host sends a TCP ACK packet to another host and if a TCP connection has not been established between them, a TCP reset (TCP RST) message with the fixed length of 40 bytes is sent in response regardless of the size of the TCP ACK packet. Since TCP ACK and RST packets traverse the network in two different directions (i.e. TCP ACK packets are transmitted from the bandwidth measurement server to hot-spot Access Points (downstream link), and TCP RST packets are transmitted in the reverse direction (upstream link)), either downstream or upstream link can be saturated by adjusting the size of the TCP ACK packet or the interval between back-to-back ACK packets. HSBE consists of increasing the sending data rate either by increasing the ACK packet size or the interval between back-to-back ACK packets and observing the RTT. RTT of a TCP ACK packet is the time elapsed between the time when the ACK packet is sent and the time when the corresponding RST packet is received. The bandwidth of the saturated link is then estimated as the sending data rate when RTT starts increasing from being same. The technique is highly scalable and provides accurate results with low latency and intrusiveness. Results: The available bandwidth latency shows a linear increasing trend where lower bandwidth shows higher estimation latency when an instance uses same size of TCP ACK packets to estimate bandwidth. Only 10% to 20% TCP packets generated from the client side experience longer transmission delays. Applications: HSBE is used in commercial hot-spots that offer Internet access over a wireless network. 3.2.14
Available Bandwidth Estimation in IEEE 802.11-based Wireless Networks (ABE11WN)
Problem Statement: ABE11WN[97] estimates per-neighbor available bandwidth for IEEE 802.11-based wireless networks by gauging the effect of phenomena such as medium contention, channel fading and interference. Methodology: Separate throughput to different neighbors of transmitting a packet at MAC layer is measured because the channel conditions may be very different to each one. This link 34
layer measurement mechanism captures the effect of contention, fading and interference errors on available bandwidth because if these errors affect the RTS or DATA packets, they have to be re-transmitted. This increases the time interval between the channel busy and contention time and correspondingly decreases available bandwidth. Using average throughput of past packets, current bandwidth is estimated which is feasible and robust. Result:
At a very low-cost and with high probability, every flow in the network receives
at least its minimum requested share of the network bandwidth. It also shows that the explicit rate allocation scheme can effectively utilize the bandwidth resource of the wireless links. Applications: ABE11WN is used for admission control and rate control of flows sharing the network. 3.2.15
Bandwidth Estimation based on Retransmission (BER)
Problem Statement: BER[98] estimates link available bandwidth by using frame retransmission prediction. Methodology: Nodes broadcast Hello messages periodically, which includes channel utilization information. At the same time, nodes evaluate the collision probability and retransmission count according to the average size of the contention window. Link is affected by several factors including link asymmetry, frame collision, frame retransmission, node contention. The link available bandwidth is estimated by combining these results with the related local backoff process information. This is accomplished with the method of cooperation between mobile nodes. Result: BER estimates the frames collision status and available bandwidth accurately under different network loads. Applications: BER is used in ad hoc network for effective usage of resources.
3.3
Comparison of PT Protocols
In this section, we provide the summary of various available bandwidth techniques under PT in Table 3. In the table, the meaning of the various columns, left to right, are same as in Table 2 with little modification as including estimation of available bandwidth at node and excluding Number of probe packet column.
4
Techniques Only for Wireless Networks (TOWN)
The bandwidth bottleneck in a commercial wireless environment lies on the edge of the Internet. The available downstream bandwidth is usually larger than the available upstream bandwidth, and the available bandwidth remains relatively persistent and piecewise stationary[99]. These concepts do not apply directly to wireless MANETs because the idea of a point-to-point link does not exist as an independent communication resource between a pair of neighbor nodes given the shared nature of the transmission medium, and the random nature of multiple access protocols. Theoretical analysis of the capacity, the bandwidth and the available bandwidth of a link/path in MANET proves that they should extend the wired concepts and capture their 35
Table 3: Comparison of PT Protocols GPT
Techniques EABRRL TCPV TCPW QoS-AODV
PPT
Path √
BE at Accuracy Link Node √ High
√
BE Time
Overhead
Innovation
Low
Low
Two streams of ICMP timestamp packets are used to compute queuing delay expected flow rate and the actual flow rate based on RTT are used Usage of rate of acknowledgments Broadcast ABW computed from local resources as function of ratio between the numbers of transmitted and received packets using Hello message in MANET Consider interferences in MANET without control entity Neighbour capacity-testing in cs-range (two hops) before session admission; Consideration of intra-route contention between cs-neighbours Consideration of various resource retrieval ranges; Usage of waiting time and backoff, channel idle time, measurement period, and maximum capacity Same as AAC with usage of collision probability Differentiate the channel busy caused by transmitting or receiving from that caused by carrier sensing Considers the additional overheads caused by ACK frames, which are not considered in AAC and AAB Similar to cPEAB in addition with the overhead caused by the RTS and CTS Usage of Software agent in 4G wireless network Usage of total busy period which includes frames transmission time, frame intervals and backoff durations for the channel within the monitoring period in distributed manner Usage of two methods based on used and Hello packet available bandwidth from ratio of free and busy times Similar to QBEM Usage TCP ACK and RST packets for bandwidth estimation different access points Usage of the effect of phenomena such as medium contention, channel fading and interference Parallel-transmission using passively monitoring local transmission activities Usage of channel utilization, the related local backoff, the collision probability and retransmission count
√
√
High
Low
None
√
√
High Medium
Low Low
None None
BRuIT CACP
√
√ √
√ √
Medium Medium
Varies Varies
Low Higher
AAC
√
√
√
Medium
Low
None
√
√ √
√ √
Low High
Low Low
None None
cPEAB
√
√
√
Varies
Varies
None
APBE
√
√
√
High
Low
Low
√
√ √
√
High High
Low Low
Medium Low
√
√
High
Medium
Low
High High
Medium Low
None Low
√
High
Low
None
DCSPT
√
High
Low
Low
BER
√
High
Low
Low
ABE IAB
ABEWN DABE
QBEM DBE HSBE
2-hop path √ √
ABE11WN
√
√
√
random shared nature and their packet length dependency[100]. APT cannot be used in such an environment as it relies on probing traffic that impacts the wireless communication services due to the additional data introduced. The available bandwidth for wireless network is estimated using separate protocols categorised as CLT or MBT, which are discussed in this section.
4.1
Cross Layer-based Techniques (CLT))
CLT requires modifications in the devices and standard protocols to measure available bandwidth, which is the difficult task. Still this approach is used for many efficient usages of the network resources in wireless network[4][44] as CLT has the lower overhead than APT explained in section 2. Most of CLT are used for QoS provisioning in the scalable video streaming of highdefinition content. In this section, we discuss CLT protocols such as SDSBE[102], iBE [22], TIBET[103] and CLDEBE[104]. IdleGap[101] CLT tool estimates the available bandwidth of a wireless LAN based on the information from a low layer in the protocol stack.
36
4.1.1
Service Differentiation Supported Bandwidth Estimation (SDSBE)
Problem Statement: SDSBE[102] estimates the bandwidth by considering the interference of neighbor nodes as well as the flow type in MANETs. Methodology: Service differentiation supported queue model shown in Figure 3 is used at the MAC layer. The traffic is divided into two prioritized classes: the high prioritized real-time traffic (RT) and the low prioritized best effort traffic (BT). All the traffic arrived at a node enters into the corresponding virtual queue according to their traffic type at first. Then the scheduler decides how to contend for the wireless channel. The different traffic is scheduled according to their priority. For same priority traffic, FIFO scheduler is used. N1
N2
Nn
RT Queue
BT queue
Neignbours of node i
Queue of node i
Wireless Channel
Scheduler
Node i Wireless Channel
Figure 3: Service Differentiation Supported Queue Model The two different traffic types have the different service level. The available bandwidth for different prioritized traffic depends not only on capacity of the medium relaying nodes, but also on the same and higher prioritized aggregate traffic. When a new arrival flow begins to contend the wireless channel, the lower prioritized traffic cannot degrade the service level of existed same and higher prioritized traffic, but the higher prioritized traffic can sacrifice the performance of lower traffic to get better service. The maximum available bandwidth of RT is defined as the maximum transfer rate under the condition of having no influence on the other RT. Result: The estimated result with delay model (that includes not only the RT, but also the BT) which takes the data service rate as the bandwidth metric is higher than the real value. Applications: SDSBE is used in multimedia applications in MANETs. 4.1.2
Intelligent Bandwidth Estimation (iBE)
Problem Statement: The iBE [22] estimates the wireless network bandwidth using the packet dispersion technique by recording the packet payload size and OWD at the MAC layer for multimedia delivery over the IEEE 802.11 wireless networks. Methodology: The iBE make use of the difference between the packet transmission time and reception time at MAC layer. It records the packet payload size and OWD at the MAC layer and uses the application data packets themselves instead of probing traffic, reducing the 37
estimation overhead. However, iBE requires modification of the 802.11 MAC protocol. The estimation results are then sent to the application layer for intelligent adaptation. Results: The average bandwidth estimated by iBE for one server and one client is 3.52 Mbps and measured bandwidth value is 2.96 Mbps. The bandwidth estimated by iBE is always closer to the measured bandwidth than that of Spruce. Applications: The iBE is used in multimedia-based services such as live multimedia streaming, Video-on-demand (VoD), IPTV, etc over the wireless networks. 4.1.3
Time Intervals based Bandwidth Estimation Technique (TIBET)
Problem Statement: TIBET[103] estimates more accurate, unbiased and stable end-to-end bandwidth for TCP needed for a fair sharing of the network resources. Methodology: TIBET requires modifications within the TCP congestion control procedure only at the sender-side of a connection since there is no need for cooperation from the peer TCP. It estimates the bandwidth used by the TCP source, even in the presence of packet clustering and ACK compression and also enables the TCP connections to track changes in the available bandwidth quickly. The estimate of the average bandwidth is the ratio of a run-time sender-side estimate of the average packet length to the average inter-departure time. It is applied either to the stream of transmitted packets or the stream of received ACKs. Results: TIBET is more accurate, unbiased and stable bandwidth estimates, needed for a fair sharing of the network resources. Applications: TIBET is used within the congestion control scheme of TCP for wireless network. 4.1.4
Cross-layer Designed Effective Bandwidth Estimation (CLDEBE)
Problem Statement: CLDEBE[104] estimates bandwidth requirements of different connections in broadband multimedia satellite networks with Adaptive Forward Error Control (AFEC). Methodology: AFEC may cause dynamic variation in the actual transmission rate of traffic sources that should be taken into account when the effective bandwidth is estimated. The actual transmission rate for a connection in satellite network is Ce × r(t) where Ce is the
effective bandwidth of connection, which can be resolved through fluid approximation and r(t) is time variant code rate depends on the number of states of the connection in the satellite networks. States of r(t) has transition matrix Q, π(x) is a cumulative density function vector of the queue length x for the state space. We know that
dπ(x) dx
× D = π(x) × M by standard fluid
approximation method where D is the drift matrix and M is the generator matrix. Solving this, π(x) = M −1 D.
P
zi ≤0 (ai
× exp(zi x) × φi ) where zi is eigenvalue and φi is left convector of matrix
Largest eigenvalue is usually chosen as the effective bandwidth Ce . When the number
of r(t) is large, it is hard to obtain the value of Ce analytically. The author suggested simple method for modifying Ce as Cm = Ce /ρ where the factor ρ can be i)first state rate or ii) last state rate or iii)mean rate of all states.
38
Results: The bandwidth estimated by TIBET is very close to the correct value. Result with the mean rate of all states for estimating bandwidth is a good choice. The bandwidth estimate is not affected by the ACK compression effect. Applications: CLDEBE is applied to estimate the effective bandwidth values for general time-varying satellite channels and satellite communication network with multiple-code-rate AFEC.
4.2
Model based Techniques (MBT)
The available bandwidth approaches that utilize currently sensed information as explained in previous sections are often insufficient because they lack predictive power and scalability, just considering that the entrance of a new flow results in the change of network parameters (i.e. collision probability) and further the real available bandwidth. Analytical/Mathematical models help in providing the quantitative analysis of the protocols, helping us to predict the result set if the network parameters are changed. This is not possible in either APT or PT. There have been few analytical models proposed using the operation of DCF in ad hoc networks each with their own set of assumptions. Mathematical model mechanism sends packet trains at a rate lower than the available bandwidth. MBT is very useful for network performance analysis, but the challenge is that to build an accurate analysis model for multi-hop wireless network is not an easy job. Depending on the congestion level of the network, an IEEE 802.11 network can be in one of the three states: saturated, unsaturated or semi-saturated. A network is in a saturated state when every node is saturated, which usually means that the network is overloaded. In a saturated network with n transmitting nodes, every node always has packets to transmit that fills up the network bandwidth. In an unsaturated network, no node is saturated, which indicates a lightly loaded network. A semi-saturated network is between the saturated state and the unsaturated state, where some of the nodes are saturated while other nodes are unsaturated. In this section, we discuss MBT protocols such as MEEBEMHN[105], BETCPDT[42], STABE[106], MBE[41], DBM[21], ARCH [107] and AWMM[108]. 4.2.1
Model Based End-to-End Bandwidth Estimation in Multi-hop Network (MEEBEMHN)
Problem Statement: MEEBEMHN[105] estimates the bandwidth in order to guarantee throughput to applications in multi-hop wireless networks. Methodology: A comprehensive model given by the author accounts for interference, collision, hidden node problem, capture effect, non-ideal channel as well as non-saturated conditions in a multi-hop network environment. This model is used to perform admission control and end-to-end available bandwidth named MEEBEMHN in order to guarantee throughput to applications in multi-hop wireless networks. MEEBEMHN uses a binary search algorithm. It starts by checking half of the theoretic maximum capacity, and if it is sufficient, a flow with this bandwidth can be admitted without impairing the QoS of existing flows. It continues to search for a higher admissible bandwidth in the upper half, otherwise it searches in the lower 39
half. This process halves the search space each time, and the search continues until the search space is less than a threshold. Result:
The available bandwidth does not decrease linearly with the increment of inter-
ference. The available bandwidth of a given path in the absence of other interference varies with the increase of the hop number. Applications: MEEBEMHN is used in bandwidth-sensitive applications such as video streaming, video conferencing or network gaming. 4.2.2
Bandwidth Estimation IEEE 802.11 TCP Data Transmissions (BETCPDT)
Problem Statement: BETCPDT[42] develops the application layer model to predict the maximum achievable bandwidth for TCP-based data transmissions over IEEE 802.11 WLANs using characteristics like transmission error, contention and retry attempts. Methodology: BETCPDT updates the TCP throughout model by adding three steps: 1) packet loss (due to queue overflow-related loss and transmission loss) update; 2) RTT update; 3) combination of TCP model and 802.11DCF model. It considers both TCP congestion control mechanism and IEEE 802.11 contention-based channel access mechanism. The available bandwidth process at the server estimates the achievable bandwidth using two types of parameters: data size to be sent and the feedback information from receivers, i.e. packet loss rate and the number of clients. Results: The two-tailed T-test analysis demonstrates that there is a 95% confidence level and there is no statistical difference between the results from BETCPDT and the results from the real test. BETCPDT achieves higher accuracy and lower standard deviation of bandwidth, in comparison with other state-of-the-art available bandwidth schemes. Applications: BETCPDT used for QoS of multimedia services in IEEE 802.11 WLANs. 4.2.3
System-Theoretic Approach to Bandwidth Estimation (STABE)
Problem Statement: STABE[106] estimates the available bandwidth from the measurements of network probes related to potential non-linearities of the underlying network - min-plus linear system of the network calculus - for end-to-end paths. Methodology: A network is viewed as a min-plus linear or nonlinear system that converts input signals (arrivals) into output signals (departures) according to a fixed, but unknown service curve S in STABE. The service curve of the network expresses the available bandwidth as a constant-rate or a more complex function. The service curve is estimated based on measurements of a sequence of PP or passive measurements of a sample path of arrivals. The arrival A(t) and departure D(t) functions of a probe are constructed from timestamp of the transmission and reception of packets and from knowledge of the packet size. A system is min-plus linear if it can be described by an exact service curve and the available bandwidth problem is reduced for solving the inversion of D(t) = A × S(t) for all t >= 0. Three available bandwidth methods are
given in STABE as min-plus linear systems: i) Passive measurements - It can only be applied
to linear networks ii) Rate scanning - APT that transmits packet trains at a constant rate, but 40
varies the rate of subsequent trains and iii) Rate chirps - BEs are based on the measurement of a single packet train, with a geometrically decreasing inter-packet spacing, which corresponds to an increase of the transmission rate. By interpreting a network as a system that is min-plus linear at low loads and nonlinear when the network load exceeds a threshold, the crossing of the linear and nonlinear regions marked as the point where the available bandwidth is observed. Results: Rate scanning provides more reliable estimates of the service curve than rate chirps. The higher variance of the cross-traffic results in a higher variability of the service curve estimates. For the available bandwidths of links with constant-rate functions, the convolution over multiple links is equal to the minimum of the rates. Applications: STABE is used to find bandwidth availability from traffic measurements in the network. 4.2.4
Model-based Bandwidth Estimation algorithm (MBE)
Problem Statement: MBE[41] estimates the available bandwidth analytically based on novel TCP/UDP throughput models for wireless data communications. Methodology: MBT does not require either probing traffic or modification of MAC protocol. MBE estimates TCP and UDP traffic separately. The behaviors of the TCPs fast retransmission and timeout mechanisms are captured to estimate the maximum bandwidth share that a TCP connection could achieve. Three steps to update the original TCP model are: 1) packet loss probability update; 2) RTT update; 3) consideration of both TCP and 802.11 DCF models. TCP fast retransmission and timeout are removed in MBEs UDP version. Results: MBE model is robust under different conditions: variant packet size, PER and dynamic wireless link. MBE achieves 47% less estimation error rate than IdleGap and 18% lower overhead than iBE. Additionally, MBE produces the lowest standard deviation and mean value for both error rate and overhead. Applications: MBE is used for multimedia services over IEEE 802.11 networks. 4.2.5
Delay-based model (DBM)
Problem Statement: DBM[21] infers bandwidth based on the measurement of the performance metrics of Poisson and periodic active probing. Methodology: The construction of the measurement model involves three components: the design of the probing sequence, the characteristics of the cross-traffic stream and how packets are transmitted in the network. Probing sequences with three types of inter-arrival time distributions are commonly used: a Poisson probe sequence (provide unbiased near-continuous detection), a periodic probe sequence (easier to implement without having to concern about the stochastic robustness of a Poisson sequence) and an exponential probe sequence (proposed to capture the long-term dependence of the Internet traffic but lacks an accurate analysis for understanding its stochastic behaviors). The model-based available bandwidth measurement using delay-variation based on the squared coefficient of variation of the inter-departure time between two consecutive PP (SVCProbe) is used here. 41
Results: The performance comparison of the available bandwidth measurements based on loss models, and delay models indicates that the delay-based measurement exhibits many advantages over the loss-based measurements, such as accuracy, overhead, and robustness. SCVProbe achieves similar or even better measurement accuracy than Pathload with much less probing time and smaller overhead. Applications: DBM is used to understand network congestion and enhance the performance of QoS demanding applications. 4.2.6
AutoRegressive Conditional Heteroscedasticity Time-series Model (ARCH)
Problem Statement: ARCH [107] models available bandwidth behavior from a time-series analysis prospective. Methodology: ARCH time-series model provides a probabilistic QoS channel specification in terms of coefficients that allow rapid computation of “crossing probability - the probability that the available bandwidth drops below the QoS critical threshold for the period of time required for the real-time task execution. The network path characterized by model coefficients β0 and β1 which are evaluated based on observed available bandwidth behavior over a reasonable period of time and are updated at run-time reflecting the changing dynamics of a network environment. The critical value and the time-frame should be defined by the QoS specifications of a particular real-time task. Results: ARCH needs at least 90 observations of the available bandwidth time-series for making accurate estimation of model parameters, which corresponds to the time-period of 1.5h. The accuracy of the “crossing probability” prediction may vary from path to path and over time. Applications: ARCH is used in applications that support remote real-time task execution. 4.2.7
Adaptive Wavelet-based Multi-fractal Model (AWMM)
Problem Statement: AWMM[108] estimates effective QoS-aware bandwidth for multi-fractal network traffic flows by using properties of the wavelet coefficients of multi-fractal cascade processes. Methodology: The triple (Lipschitz exponents, semivariogram, correlation coefficients) parameter of the AWMM are obtained through the scaling function and the moment factor. These triple is updated periodically for online effective available bandwidth. The value of the moment generating function M (k) of the traffic flow is also updated at time instant k. The value of the effective bandwidth at the time instant k is Lipschitz exponent that is measured using the moment generating function. Results: The adaptive effective bandwidth closely matches with bandwidth that obtained via the static computing approach. This adaptive method is a less time consuming approach, requiring minimal memory storage. Applications: AWMM is used for call admission control and resource allocation within computer network environments to support the flow at the required QoS. 42
4.3
Comparison of TOWN Protocols
In this section, we provide the summary of various available bandwidth techniques under TOWN as shown in Table 4. In the table, the meaning of the various columns, left to right, are same as in Table 2. Table 4: Comparison of TOWN Protocols BE at
CLT
Techniques
Accuracy
BE Time
Overhead Number of PP
Innovation
Service differentiation queue model at MAC layer used for neighbour interference Recorded the packet payload size and OWD at the MAC layer
Path
Link
SDSBE
√
√
High
Low
None
iBE
√
√
High
Low
Low
√
High
Low
none
√ √
High Medium
Low Low
None None
MEEBEMHN
√
High
None
None
BETCPDT
√
High
Low
None
High
None
Varies
PPRCH
MBT
TIBET CLDEBE
√ √
Application data packet dispersion as probe packet to compute OWD Packet pair
STABE
√
MBE
√
√
High
None
None
DBM
√
√
High
Low
Low
ARCH
√
Varies
Medium None
AWMM
√
High
Low
5
√
Sequence of PP
One of the Poisson, exponential, or periodic probing
None
Interference-aware approach which is implemented in Linux kernel sender-side TCP congestion control Computation of Adaptive Forward Error Control model uses abinarysearchalgorithm and accounts for interference, collision, hidden node problem, capture effect, non-ideal channel as well as non-saturated conditions Updates TCP throughout model for combining TCP and 802.11DCF model and updating packet loss and RTT min-plus linear or nonlinear system converts input signals arrivals into departures according to a fixed unknown service curve, which is estimated based PP or passive measurements of a sample path of arrivals Neither probing traffic nor modification of MAC protocol is required. TCP/UDP throughput models modified Consideration of delay-variation based on the squared coefficient of variation of the inter-departure time between two consecutive PP time-series model provides a probabilistic QoS channel specification in terms of coefficients Consideration of multi-fractal network traffic flows and wavelet coefficients
Other Bandwidth Estimation Techniques (OBET)
We found many techniques that do not follow any of specific categories. We considered OBET category for such techniques. In this section, we discuss OBET protocols such as ImTCP[109], BEMV[110], SABE[111], TREND [23] and FBETNAS [112].
5.1
Inline measurement TCP (ImTCP)
Problem Statement: ImTCP[109] actively estimates the available bandwidth along a network path at TCP layer using data packets and ACK packets of a TCP connection. Methodology: ImTCP locates at the bottom of TCP layer as shown in Figure 4. Data packets and ACK packets of a TCP connection (inline measurement) are utilized for the measurement, instead of PP. When a sender transmits data packets, ImTCP first stores a group
43
of several packets in a queue and subsequently forwards them by adjusting the transmission intervals of packets to form packet streams that are group packets sent at a time, for the measurements. Each group of packets corresponds to a probe stream. Then, considering ACK packets as echoed packets, the ImTCP sender estimates the available bandwidth within the given search range. The relationship between the transmission rate and the arrival rate of the packet as two straight lines using the linear regression method is approximated. The slope of the line determines that the highest transmission rate in line with small transmission rates is the value of the available bandwidth. To minimize transmission delay caused by the packet store-and-forward process, an algorithm for RTT-out calculation in TCP to regulate packet storage time in the queue is used. TCP Layer TCP Protocol Processing Data Packets Measurement Program
ImTCP
REcord the arrival time Calculate results
Buffer
IP Layer
ACK Packets
Network Interface
Figure 4: Placement of measurement program at ImTCP sender Result: ImTCP does not degrade TCP data transmission performance, has no effect on surrounding traffic and yields acceptable measurement results in intervals as short as some RTTs. Applications: ImTCP used in a network path that plays an important role in adaptive control of the network.
5.2
Bandwidth Estimation for Multiplexed Videos (BEMV)
Problem Statement: BEMV[110] estimates bandwidth requirement for any number of multiplexed videos using analytical technique. Methodology: Multinomial model used in BEMV is built on a Markov-modulated gamma (MMG)-based traffic model of a single video. It takes into consideration average frame size variations in different segments of a video and predicts a multiplexing gain for any number of multiplexed videos. Thus, segments associated with smaller frames require lower transmission bandwidths, and those with larger frame sizes need higher bandwidths. If Bavg is the average bandwidths necessary in state si , the average transmission rate or bandwidth for the whole video is a weighted average of Bavg based on the probability of the state. Results: The error in bandwidth prediction is low and decreases as the number of video 44
increases. The estimated multinomial bandwidths up to 20 videos computed by exact and approximate means are indistinguishable indicating high accuracy of the method. Applications: BEMV is used in VoD applications.
5.3
Statistical Aggregate Bandwidth Estimation (SABE)
Problem Statement: SABE[111] estimates the bandwidth for smoothed Variable Bit Rate (VBR) video streams. Methodology: This model is based on concepts of multi-class networks of queues whose service centers are represented by the bandwidth levels assumed by smoothed films. The mean service times and the transition probabilities between service centers are directly derived from real video traces. The exact bandwidth values of each type of the video stream produced by the smoothing algorithm which uses this queuing network model. This model derives the probability of an aggregate bandwidth based on the joint probability derived simply observing the temporal evolution of all the types of smoothed films, considering the probabilities to choose a type of the video stream. Results: Analytical bandwidth results vs. loss probability for 90 films of three different types show that the aggregate bandwidth estimated slightly overestimates the results obtained from simulation. SABE obtains better results with acceptable computational complexity. Applications: SABE is used for smoothed VBR video streams.
5.4
TREND
Problem Statement: TREND [23] estimates the available network bandwidth along a path dynamically by detecting the delay in the received video frames. Methodology: When the available bandwidth along the path is over-utilized, packets enter the path at a rate faster than the network can process them. In this case, the packets are usually stored by the network router(s) in a buffer until processing is complete. This introduces a delay in the packet arrival at the receiver, known as queuing delay. Packet loss occurs when the buffer overflows. TREND identifies bandwidth over-utilization at the delay accumulation stage before packets are lost by monitoring video packets. It searches for significant increasing and/or decreasing trends in the delay and reduces the transmitted bit-rate accordingly. Results: TREND algorithm converges to 90 to 95% of the available bandwidth in up to 15 seconds with no packet loss. Once the bandwidth is over-utilized it takes 9.65 frames on average to detect it. Results: TREND is used in video calling applications over the Internet.
5.5
Fast Required Bandwidth Estimation Technique for Network Adaptive Streaming (FBETNAS)
Problem Statement: FBETNAS [112] determines whether the required bandwidth is available by using relative OWD trend and the temporal coding of SVC.
45
Methodology: FBETNAS has required bandwidth probe (RB-Probe) at streaming server and each client side. Streaming server RB-Probe controls generation and transmission of PP. Streaming server also has a real-time adaptive encoder that controls the frame rate according to network state. Streaming server transmits original media packets and periodic PP to each client. The PP used to determine whether the network can accommodate the corresponding bit rate. The client RB-Probe collects a fixed number of original media packets and PP. The client analyzes the relative OWD trend based on collected time gaps between media packets or PP. It sends the result to the streaming server that adjusts the media bit rate based on the relative OWD trend analysis. If the required bandwidth exceeds the available bandwidth, the streaming server reduces the media frame rate. However, if the relative OWD is not increasing, it maintains the current frame rate. Results: Using the RB-Probe, the cost of measuring the available bandwidth is averages of 44ms whereas the cost in pathload is 3773ms. Results: FBETNAS is used in high-quality media streaming service such as real-time high quality and multi-dimensional broadcasting and VoD through IP set-top box (STB) or digital TV.
5.6
Comparison of OBET Protocols
In this section, we provide the summary of various available bandwidth techniques under OBET as shown in Table 5. In the table, the meaning of the various columns, left to right, are same as in Table 2. Table 5: Comparison of OBET Protocols BE at
OBET
Techniques
BE Time
Overhead Number Innovation of PP
ImTCP
Path Link √
Medium
Varies
Varies
BEMV
√
√
High
None
None
SABE
√
√
High
Low
Low
High High
Low Low
None Medium
√ TREND √ FBETNAS
6
Accuracy
periodic PP
TCP layer using data packets and ACK packets of a TCP connection Multinomial model built on a Markovmodulated gamma-based traffic model of a single video smoothing algorithm which uses multiclass networks of queues delay in the received video frames fixed number of original media packets and PP used to analyze the relative OWD trend, which adjusts the media bit rate
CONCLUSIONS
Bandwidth estimation is a significant issue in communication networks because each host in a network has imprecise knowledge of the network status, and links change dynamically. The approaches published by researchers have been categorized into groups. This paper has, firstly, provided various terminologies used for the same technique and problem with it. Based on the literature of bandwidth estimation in communication networks, we have provided the taxonomy 46
for the published techniques, which focus on active probing techniques, passive techniques, techniques only for wireless networks, and other available bandwidth techniques. Under each category, a comprehensive survey of BE techniques found in the literature have been discussed by highlighting the problem statement, methodology adopted, result analysis and applications. These techniques have their own advantages and limitations in terms of accuracy, time taken for estimation of bandwidth and overhead on the networks which is highlighted at the end of every category of proposals along with innovation in it. The operation of 55 protocols was summarised. No clear consensus has been reached, which provides the accurate estimation of the available bandwidth. This survey article on bandwidth estimation may be useful to the researchers working in the related area.
Acknowledgments The authors wish to thank Visvesvaraya Technological University (VTU), Karnataka, INDIA, for funding the part of the project under VTU Research Scheme (Grant No. VTU/Aca./201112/A-9/753, Dated: 5 May 2012.
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