Energy Aware Cross Layer Uplink Scheduling for ... - IEEE Xplore

Globecom 2013 Workshop - The 5th IEEE International Workshop on Management of Emerging Networks and Services

Energy Aware Cross Layer Uplink Scheduling for Multihomed Environments Takoua Ghariani, Badii Jouaber Institut Mines-Telecom/Telecom SudParis - CNRS UMR 5157 SAMOVAR {Takoua.Ghariani,Badii.Jouaber}@telecom-sudparis.eu

Abstract—With the continuous evolution of wireless technologies, there is today a plethora of access networks ranging from wireless hotspots to high speed cellular networks. Within this rich environment and with multimodal terminals that are able to connect to different wireless technologies at once, users are expecting to benefit from higher throughput and enhanced QoS. However, the coexistence of several wireless systems rises different technical challenges including interface selection, packet scheduling, multi-path routing and mobility management. At the network level, recent protocols such as MTCP and mSCTP allow session continuity and offer the possibility of seamlessly adapting IP routing during a transmission session. These protocols are mainly based on the use of a set of IP addresses that can be associated to the same terminal and user session. But at the access level, efficient packet scheduling mechanisms on multiple interfaces are still required. New rules and approaches are needed to select the most suitable technology that better fits QoS requirements considering the dynamic mobile user environment. In addition, optimizing energy consumption should also be considered in order to lengthen mobile terminals’ battery lifetime. In this paper we propose an energy efficient cross layer scheduling mechanism for multi-homed wireless terminals. The proposed algorithm goes through the use of energy models for both 802.11n and LTE technologies to dynamically select the more suited technology according to users’ context. Performance evaluations of the proposed algorithm are presented and discussed. They show that a significant energy saving can be achieved while respecting applications QoS requirement. Index Terms—Energy Consumption, Energy Aware, Scheduling, Interface Selection, Green Networks, Multipath TCP, SCTP, Wireless Networks, LTE, 802.11n, Multihoming.

I. I NTRODUCTION With the continuously increasing number of smartphones and portable devices, new mobile services are emerging almost every day. Mobile Internet is becoming a basic service, offered through a plethora of overlapping heterogeneous wireless access networks. Furthermore, thanks to multi-path based protocols such as mSCTP (mobile Stream Control Transmission Protocol) [2] or MPTCP (MultiPath Transmission Control Protocol) [3], mobile terminals are expected to be able to use simultaneous connections and/or to seamlessly switch between different access technologies during a communication sessions. With some cost, these rising protocols allow the use of multiple IP (Internet Protocol) paths for TCP sessions and can enable seamless session mobility. 978-1-4799-2851-4/13/$31.00 ©2013IEEE

On the one hand, the availability of multiple wireless access technologies in one area can enhance the Quality of Experience (QoE) of users since it can provide higher throughput and service availability. On the other hand, the simultaneous use of multiple network interfaces on mobile terminals can not be efficient when considering the limited battery capacity that still represents a major constraint. An example of such scenario is givin on figure 1, where different Wifi and Cellular coverage areas are overlapping.

Fig. 1: Multihomed environement

According to [1], users are more and more aware about their smartphone energy consumption limitations and there is a need for automated features to reduce and optimize energy consumption on mobile devices. Several factors can affect battery lifetime on mobile devices. Channel conditions may be the most important one when considering user mobility. Therefore, accurate energy models are helpful in understanding these aspects and can allow to predict and to optimize energy consumption and data scheduling. This paper is intended to propose a new scheduling algorithm that combines both QoS and Energy consumption to increase QoE efficiency. It is structured as follows: section 2 is dedicated to analyze and discuss the related work. In section 3, energy models are proposed for two wireless technologies: 802.11n and LTE. The proposed scheduling algorithm is presented in Section 4. Section 5 is dedicated to performance evaluations of the proposed scheduling algorithm and Section 6 concludes this paper.

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II. R ELATED WORK

III. P ROPOSED S OLUTION

In the literature, there are different studies related to the impact of applications on energy consumption over different wireless technologies. These range from analytical modeling to real measurement. In [8], authors studied the impact of packet sizes on the energy consumption using an analytical model. They proposed a model for energy consumption per successfully transmitted packets. The main objective of the proposed model is to optimize the fragmentation size of data packets considering the energy cost and taking into account channel conditions. In [11], a comparative study is presented for energy consumption in GSM, 3G and WiFi technologies. Based on measurements, authors propose TailEnder: a protocol aimed to reduce energy consumption for delay tolerant applications. This protocol minimizes the time duration spent by mobile terminals in the high power state and is based on prefetching techniques and the use of proxies. In [13], a quantitative study based on measurements is proposed for the specific service of YouTube access on mobile devices. Energy consumption is investigated for both WCDMA and WLAN access technologies and results show that WLAN interfaces consume less energy than cellular ones, for the considered use case. In [4] authors propose an energy consumption model for 3G and WLAN terminal interfaces. A distinction between signaling and transfer energy costs is made. The paper show that (i) energy consumption for data transfer is function of time (duration) in the case of file transfer and that (ii) it depends on the distribution of packet data sizes and on the available data rates. Extending the battery lifetime on mobile devices is still challenging although several advances have been already done on batteries technologies. Efficient energy management is still required at different levels such as network selection and scheduling. In [9], a communication protocol that uses the WWAN paging technique in order to manage WLAN interface states is proposed. For power saving, authors propose to wake-up these interfaces when there is an incoming long-lived multimedia data traffic and to turn them to the Idle state when traffic in low. Decision to turn the wireless interfaces to the Idle/Active states is based on the use of thresholds regarding the number of packets in the Radio Network Controller (RNC) buffer. Authors in [10] propose an energy aware scheduling algorithm for MPTCP. The limitation of their proposed algorithm is that they use a simple model for they interface selection decision where only the throughput is taken into account. Optimizing energy consumption requires knowledge from different layers including MAC, PHY as well as informations about the system (ie. load, number of active stations,..). We propose an energy aware cross layer uplink scheduling mechanism that takes benefit from related MAC and PHY layers parameters and measurements.

In this section, let’s consider a User Equipement (UE) with two wireless interfaces using respectively LTE and 802.11n technologies. We assume here that the two wireless interfaces are always ON and therefore, that there is no energy cost for connection establishments. The proposed solution is based on energy models for LTE and 802.11n presented below. A scheduling solution that optimizes both QoS and Energy consumption is then built and evaluated. A. LTE Energy Model The 3rd Generation Partnership Project 3GPP has defined the Single Carrier Frequency Division Multiple Access (SCFDMA) as the access technique for uplink channels of LTE networks. The LTE air interface is based on resource allocation units denoted as Physical Resource Blocks (PRB) consisting of 12 adjacent subcarriers. Every TTI (Transmission Time Interval), the eNodeB allocates a certain number of PRBs per user according to its needs and available resources. The duration of one TTI is 1ms. For SC-FDMA, there are two types of subcarrier scheduling schemes: Localized FDMA (L-FDMA) and Interleaved FDMA (I-FDMA). In L-FDMA, only consecutive sub-carriers are assigned to a particular user. However, in I-FDMA, users may be assigned sub-carriers that are distributed over the entire frequency band. As in [14], we consider here resource allocations based on the contiguous PRB allocation The proposed LTE energy model considers cross layer informations and includes several parameters from the MAC and PHY layers. The energy cost for sending one packet is expressed as : ELT E = PLT E × t (1) Where t is the time needed to send a packet through the LTE network and PLT E is the standardized uplink power [12] defined as : PLT E = min{PM AX , P0 + 10log10 N + αP Lt + δmcs + f (∆i )} (2)

PM AX denotes the maximum transmit power. It depends on the UE power class. N is the number of PRBs (Physical Resource Block) allocated to the UE. P L is the pathloss measured by the UE. α and P0 are cell parameters broadcasted from the eNodeB to the UE. We consider here an Open Loop Power Control scheme. After measuring the pathloss, the terminal can estimate its transmission power and then sends a feedback to the eNodeB which sends back δmcs and f (∆i ) to the UE. We assume that the resource allocation mechanism attributes equal numbers or resource blocks to each user.The Minimum allocation to a single UE during a subframe (1 ms) are 2 PRBs with one PRB in each slot of the subframe If for a given bandwidth, there is a total of P PRBs, we consider the use of a Round-Robin scheduling algorithm that fairly allocates PRBs between users. If we consider M user equipments in the

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system, each UE will have P/M PRBs. The main advantages of this algorithm is its low complexity and computation cost. Each PRB is composed of 12 subcarriers by 7 SC-FDMA symbols providing a total of 84 modulation symbols. If S be the number of bits per modulation symbol for a given modulation (e.g. 6 bits in the case of 64 QAM modulation), then the number of bits that can be transmitted in 1ms can be given by : P (3) g( ) × 84 × S M Where g(x) denotes the biggest pair integer smaller than x. The number of packets that can be transmitted in 1ms is: P ) × 84 × S g( M (4) k Where k denotes the packet size. Then the time needed for a station to send one packet when M active users transmit simultaneously can be calculated by Equation(5):

n=

t=

k 1 = P n g( M ) × 84 × S

(5)

Hence the expected energy consumption used to send one packet using the LTE technology can be given by : ELT E = PLT E ×

k P g( M ) × 84 × S

(6)

B. 802.11n Energy Model For 802.11n, we consider the energy model proposed in [17] that is function of the number of active stations on the same channel. It is an accurate power consumption model at the MAC layer. This model is based on energy consumption at the different WiFi states of the DCF [18] mechanism. Regarding the Idle listening state, the model estimates both the DIFS and the backoff energy. As to the active states, collision, transmission and receiving energy are also estimated. The used parameters are described in table I. The model is function of the number of active stations M . The DIFS energy consumption is expressed as: M × DIF S Ps

probability during a packet transmission. Then, the transmission energy can be expressed as : ET rans = PT rans ×

Where E(P) is the average packet payload size. Summing up all the consumed energy, the total energy cost to transmit a packet can be obtained by: EW IF I = EDIF S + EBackof f + ET rans + EColl

(11)

Authors indicate that analytical results match real measurements. The power costs per network interface state, denoted PT rans and PIDLE , mainly depend on the hardware characteristics. In literature, many studies provide measurements for power consumption of different smartphones devices (e.g. Nokia Nseries devices and HTC G series devises). For example, in [20], authors show that the idle power can ranges from 650 to 1038 mW. C. System Model and Proposed Selection Algorithm The simultaneous use of multiple wireless network interfaces can dramatically increase the energy consumption of mobile devices. We propose in this paper an energy aware cross layer scheduling algorithm (EAS) able to assign data packets on one of the available interfaces while optimizing energy consumption. Energy consumption is based on the above described energy models. The proposed algorithm aims to optimize the use of the available interfaces while maintaining high QoS levels and reducing mobile power consumption. Algorithm 1 EAS 0: 1: 1: 2: 2:

(7)

3: 3:

Because of the random nature of the 802.11n medium access technique, and as expected when there is an important number of active stations, the waiting idle time increases and then the energy used in the Idle state increases. In [17], authors modeled the backoff energy consumption as a function of the conditional collision probability p and the maximum back-off stages m:

4: 4: 5: 6: 7: 7:

EDIF S = PIDLE ×

1 × (φ + [(8E(P )/R)/St ] × St ) (10) 1−e

8:

Begin: The mobile checks for available networks AN s with RSS > RSSth if AN exist then check if QoS > QoSth if QoS respected then Classify the candidate ANs based on their energy cost and select the minimum energy cost per interface type if minE802.11n < minELT E then Send packet over 802.11n interface else Send packet over LTE interface end if end if else Send packets over the current network end if

m

(1 − p)(1 − (2p) )) + (2p)m ) 1 − 2p (8) = PT rans × (φ + [(8E(P )/R)/St ] × St )× (9) P s × (1 − P s) × N

EBackof f = PIDLE × CWmin ( ECollision

Where N is the number of stations occurring a collision. Considering an imperfect 802.11n channel, let e be the error

When new networks, with acceptable RSS levels are available, the proposed scheduler checks if these can offer the required QoS that satisfy the application requirements. Then, the power cost together with the time duration needed to send or receive a packet are calculated. For each network interface (i.e. different technology), the available networks are classified

863


φ R St m CWmin Ps p e N

The PHY overheads The data rate OFDM symbol time The maximum back-off stage The contention window minimum The probability that a transmission occurring on the channel is successful The conditional collision possibility Error probability of a packet transmission The number of stations occurring a collision

TABLE I: Wifi energy model parameters

based on their energy cost. Then, the most energy efficient network is selected for each technology type. Finally the most efficient technology is selected for the packet transmission. We considered the QoS model proposed by [15] where four QoS parameters are used: delay (D), jitter(J), BER(ber) and bandwidth(Bwd). The QoS threshold is calculated based on [21] depending on the applications class. Qn (si) = WBer (si).Bern + WJ (si).Jn + WD (si).Dn + WBwd (si).Bwdn

(12)

Where Qn (si) is the QoS of a network n for a service i and WBer , WJ , WD and WBwd are respectively the weights of the bit error rate, the jitter, the delay and the bandwidh. Bern , Jn , Dn and Bwdn are the are respectively the values of the bit error rate, the jitter, the delay and the bandwidh. Two service classes are considered: interactive services (i.e. web browsing) and background services (i.e. emails or file transfer). Energy and QoS estimation depends on several parameters which can only be provided by networks. Hence, we need an overlay system which ghathers and communicates networks parameter values. It is provided by a distributed agents system proposed by [19] where mobile terminals and networks are presented by softwares agents in the overlay system. Reasonning and decision activities are delegated to these agents.

B. Simulation Result Figure 2 illustrates energy consumption on the terminal LTE interface when varying (i) the distance between the user and the eNodeB and (ii) the number of active stations within the system. We consider here 75 PRBs. As shown, energy consumption increases when increasing these two variables. This can be explained by the fact that the more a user gets far from its associated eNodeB, the more the pathloss increases and hence the terminal increases its transmission power (until it reaches the maximum transmission power of the mobile power class). In addition, when the number of active stations within one cell increases, the packet transmission time also increases since available resources are distributed among active stations and the required time to transmit the same data amount increases. We considered in this analysis equal distribution of resources between users.

IV. P ERFORMANCE E VALUATIONS This section provides performance evaluations, comparisons and analysis of the proposed interface selection and scheduling algorithm. A. Simulation Scenario To evaluate the proposed mechanism, let’s consider the scenario where mobile terminals evolve within an urban area according to the Gauss Markov mobility model [16]. Within this area, there are 4 LTE eNodeB and 6 802.11n base stations with overlapping coverage areas . The different parameters and variables used for the evaluations are depicted on Table II. The LTE path-loss is calculated using the COST-Hata propagation model which is applicable for urban areas and up to 2000 MHz. We assume that each network informs the mobile nodes about QoS when required based on the signal strenth valus and the number of active stations.

Fig. 2: LTE Energy Model Simulations are performed using matlab and we considered two wifi coverage levels (15% and 50%). In Figure 3, the cumulative energy consumption of a multihomed terminal is presented. As depicted, the proposed algorithm leads to a significant energy saving compared to the reference algorithm where only the signal strength is used for selecting the networks. When the mobile terminal goes through a 802.11 coverage zone, where the number of active stations is relatively high, the proposed algorithm may decide to keep the terminal connected to the LTE network, even if the signal strength of the WiFi network is higher. This leads to an important gain in energy consumption since the CSMA contention mechanism used in 802.11 technologies is very sensitive to the number of

864


LTE

802.11n

Nework Topology

Parameter System Bandwidth Number of PRBs Bandwidth per PRB Maximum transmission power PM AX P0 Path loss compensation factor α TTI Number of users per cell (M) Modulation Slot duration DIFS SIFS CWmin CWmax Active Stations Number of eNodeBs Number of 802.11n base stations

Value 5,10,15,20 MHz 25,50,75,100 180 KHz 500 mW(27dBm) -54.5dBm 0.6 1ms(2slot) [10,20,30,40] 64QAM 9 µs 34 µs 16 µs 15 1023 [2,10,20,30,40,50] 4 6

TABLE II: SIMULATION PARAMETERS

contending stations.

to switch to WiFi connections and to continue data transfer over the LTE connection.

900 800

Energy(mW,s)

700 600 500 400 Proposed algorithm

300

Signal Strength based algorithm

200 100 0 5

10

15

20

25

30

35

40

45

50

55

Number of active stations

Fig. 3: Cumulative Energy Consumption Table III shows the QoS gain of the proposed solution compaired to the reference algorithm. We consider three scenarios, the first one is when the number of active stations ready to transmit packets in Wifi is larger than in LTE. The second scenario is when LTE is more saturated than Wifi and the third one is when Wifi and LTE both have large number of actives stations. These results show that QoS of the proposed solution is always above the QoS of the reference algorithm even if the difference for the third scenario is not large since both LTE and Wifi networks are saturated. In figure 4(a) and 4(b), the number of active stations increases from 5 to 50 for the two coverage level of Wifi networks (15% and 50%). From this figure ,when the number of active terminals increases, the wasted energy ( composed of DIFS, collision and backoff associated energy) is impacted. When the number of station on the Wifi channel increases, the time spend for DIFS increases and also the probability of collision increases thereby increasing the backoff time. Moreover, the higher the Wifi coverage level of the area is, the more the difference on the wasted energy increases. This can be explained by the fact that when a mobile terminal goes through a WiFi zone, it is more efficient, in some cases, not

V. C ONCLUSION Multihoming has an important impact on mobile terminals energy consumption. Indeed, the simultaneous use and the management of multiple wireless interfaces may have dramatical impact on power efficiency. The conception of new energy efficient scheduling and network selection algorithms that correctly select the most appropriate interfaces for each data transfer is required. In this paper we proposed an energy efficient interface selection algorithm based on 802.11n and LTE energy models, using both MAC and PHY parameters as well as network loads through the knowledge of the number of active stations. Performance evaluations and comparisons to traditional decision algorithms only based on signal strength show that energy saving can be achieved while using multiple interfaces and maintaining high levels of QoS. Additional studies are still required to enhance energy models for data transfer over different wireless technologies and for efficient data scheduling over multihomed environments. R EFERENCES [1] Mikko V. J. Heikkinen and Jukka K. Nurminen ”Consumer Attitudes towards Energy Consumption of Mobile Phones and Services” in IEEE 72nd Vehicular Technology Conference: VTC2010-Fallpages 1-5, 2010. [2] R.stewart et al Stream Control Transmission Protocol , in IETF RFC 2960,, October 2000. [3] A. Ford, C. Raiciu, M.Handley TCP Extensions for Multipath Operation with Multiple Addresses,draft-ford-mptcp-multiaddressed-02, 2009. [4] E.Harjula, O. Kassinen and M.Ylianttila ”Energy Consumption Model for Mobile Devices in 3G and WLAN Networks”, in The 9th Annual IEEE Consumer Communications and Networking Conference - Emerging Consumer Technologies. , pages 532 - 537, 2012. [5] A. Rahmati and L. Zhong ”Context-for-Wireless: Context-Sensitive Energy-Efficient Wireless Data Transfer”, in In Proc. ACM MobiSys, pages 165–178, 2007. [6] T.Pering ,Y. Agarwal, R. Gupta and R.Want ”CoolSpots: Reducing the Power Consumption of Wireless Mobile Devices with Multiple Radio Interfaces”, in Proc. 4th Int. Conf. Mobile Systems, Applications and Services (MobiSys), pages 220–232, 2006.

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Wifi coverage 15% 16.18% 0.23% 0.012%

802.11n Saturated LTE Saturated LTE and 802.11n

Wifi coverage 50% 36.3% 5.9% 0.038%

TABLE III: QoS gain

2

1,2

Energy per packet (mW,s)

Energy per packet(mW,s)

1,8 1 0,8

0,6

Proposed algorithm

0,4


0,2

1,6 1,4 1,2

1

Proposed algorithm

0,8


0,6 0,4 0,2

0

0 5

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5


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a) Wifi coverage level equal to 15 %

b) Wifi coverage level equal to 50%

Fig. 4: Wasted energy consumed per packet during DIFS , collision and backoff states

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[20] Y.Xiao, P.Savolainen, A. Karppanen, M. Siekkinen and A.Yl-Jski ”Practical power modeling of data transmission over 802.11g for wireless applications”, in International Conference Series on Energy-Efficient Computing and Networking, e-Energy2010, pages 75-84, New York. [21] Yan Chen, Toni Farley and Nong Ye ”QoS Requirements of Network Applications on the Internet ”, in Journal Information-KnowledgeSystems Management archive Volume 4 Issue 1 ,Pages 55 - 76 , January 2004.

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