2011 International Conference on Innovations in Information Technology
An Adaptive Bandwidth Borrowing-Based Call Admission Control Scheme for Multi-Class Service Wireless Cellular Networks Bassel Arafeh, Khaled Day, and Nasser Alzeidi Department of Computer Science Sultan Qaboos University, Sultanate of Oman {arafeh, kday, alzidi}@squ.edu.om
Sharifa Al Khanjari Applied Information Technology Department German University of Technology in Oman (GUtech) Sultanate of Oman
[email protected]
preemption techniques according to the priorities of the traffic classes. However, maintaining the QoS in each class to avoid performance deterioration is essential. Therefore, this research introduces mechanisms for call bandwidth degradation, and mechanisms for call bandwidth upgrading based on fairness policies for resource deallocation and allocation. In addition, the scheme applies call preemption according to the class of call priorities, and call reactivation whenever the traffic conditions permit. The proposed adaptive CAC scheme introduces an online monitoring approach of the system, to determine the amount of bandwidth to be borrowed from calls, or the amount of bandwidth to be returned to calls. The performance of the proposed adaptive CAC scheme is evaluated by simulation. A discrete event simulation model is developed for the scheme, using randomly generated calls, assuming a one cell in a wireless cellular network.
Abstract—This work describes an adaptive Call Admission Control (CAC) scheme for multi-class service wireless cellular networks. The proposed scheme uses complete sharing approach of the available bandwidth among all traffic classes. The proposed adaptive CAC is achieved through call bandwidth borrowing and call preemption techniques based on the priorities of the traffic classes. The CAC scheme achieves the QoS in each class through mechanisms for call bandwidth degradation, and call bandwidth upgrading based on Min-Max and Max-Min policies for fair resource deallocation and allocation. The simulation results of the proposed adaptive CAC scheme show the strength and effectiveness of our proposed scheme compared to other schemes. Keywords-cellular networks, admission control, multimedia traffic, QoS provisioning, bandwidth allocation, bandwidth borrowing.
I.
The reset of the paper is organized as follows. Section 2 presents a brief literature review to the related works. Section 3 introduces the proposed adaptive bandwidth borrowing-based admission control scheme. Section 4 presents the system model for the proposed admission control scheme. Section 5 describes the simulation parameters and discusses the simulation results of the proposed scheme. Finally, section 6 presents our conclusions.
INTRODUCTION
The recent fast advances in wireless mobile communication technologies have led to a wider range of requirements. Currently, third generation (3G) wireless cellular networks aim to provide multimedia services in addition to voice and data. However, the challenges facing 3G and next generation cellular networks are in maintaining the quality of service (QoS) requirements for each class of the multi-service traffic in face of the limited radio resources and the mobility of users [3], [10]. On one side, there are the requirements for the traditional voice calls, which are considered as a real-time (RT) or delay sensitive service. On the other side, there is data services which are adaptive and tolerant to delay. They can operate over a wide range of bandwidth as non-real-time (NRT) or delay tolerant services. In addition, the growing demand of mobile users for integration with the global communication infrastructure (e.g., the Internet) would necessitate their demand for having reliable services similar to wired communication and data networks. Accordingly, there are many issues that should be addressed in 3G and next generation cellular networks to achieve the requirements for seamless roaming and internetworking, such as mobility management, resource allocation, call admission control (CAC), and security.
II.
In this paper, we describe an adaptive scheme for Call Admission Control (CAC) for multi-class service wireless cellular networks. The proposed CAC scheme is based on complete sharing of the available bandwidth among all traffic classes. This work proposes a CAC adaptive scheme that can be achieved through call bandwidth borrowing and call
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RELATED WORKS
Selecting Bandwidth management is an important aspect in mobile networks. An effective and efficient bandwidth management scheme is required to perform the QoS provisioning of mobile connections. However, achieving an optimum QoS provisioning remains a challenge due to the active user mobility, the limited bandwidth, the dynamic nature of wireless channels, and the computational complexity of the Call Admission Control (CAC) algorithms. In general, common handoff techniques can be divided into two categories: Non-Prioritization and Prioritization. NonPrioritization is rarely applied, since from the user’s perspective dropping a handoff call is more annoying than blocking a new call. Therefore, prioritization is mostly used, giving the handoff a higher priority than a new call [3]. Handoff prioritization schemes have different approaches such as call queuing [8], reservation [5] [6] [7], channel borrowing [4], call preemption [13], and bandwidth borrowing schemes [1] [9]. An overview of the related works using the bandwidth borrowing approach is given next.
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TABLE I. Incoming α handoff Incoming β handoff Incoming γ handoff
PRIORITIES BETWEEN THE TRAFFIC CLASSES
α active calls
β active calls
γ active calls
-
B2P2 B1 -
B1P1 B2P1 B1
B: Borrowing is allowed BP: Borrowing is allowed with possible preemption
Chang and Chen [1] have proposed a borrowing-based CAC policy that supports QoS guarantees in mobile multimedia wireless networks. In their scheme, each base station would make an adaptive decision for bandwidth reservation employing attribute-measurement mechanism and dynamic time interval reservation strategy. They have introduced a service-based borrowing strategy, in order to provide the efficient bandwidth re-distribution in a base station. The borrowing strategy is employed to re-adjust the allocated bandwidth to calls when bandwidth is borrowed or returned. The strategy allows each base station to establish several service-queues, from which borrowable bandwidth is selected in certain order based on hand-in times, the multimedia application and the amount of borrowable bandwidth for a connection. Lu and Bigham [9] proposed a utility-based bandwidth adaptation scheme for multi-class traffic QoS provisioning in wireless networks. With the proposed scheme, each call is assigned a utility function and depending on the network’s load the bandwidth of ongoing calls are upgraded or degraded so that the achieved utility of each individual cell is maximized. They also take into account the negative effects of bandwidth adaptation by integrating adaptation penalty into the utility function. III.
Basically, a new call is accepted or rejected depending on whether there are enough resources available or not, to meet the QoS requirements of the new call, without violating the QoS of the already admitted calls. In this work, QoS call level parameters at the transport layer are considered. These are the call blocking probability, the call dropping probability, and the bandwidth utilization. We assume that the multi-service cellular network traffic is classified into three main traffic classes of calls, which are referred to as alfa (α), beta (β), and gamma (γ). Class α calls have the highest priority, followed by calls of class β, then calls of class γ. The total available bandwidth (Btot) of a cell base station (BS) is completely sharable among all the traffic classes. Also, we assume that each new or handoff call request must provide the following parameters: the traffic class k (i.e., α, β, or γ), the required (i.e., maximum) bandwidth for the call (BWkreq), and the minimum required bandwidth for the call (BWkmin). In each BS of a cell, the total utilized bandwidth (Bu) is the bandwidth allocated to all active calls. In addition, the available bandwidth (Ba) is the bandwidth that has not been allocated yet to new and handoff calls; hence Btot = Bu + Ba. Accordingly, the BS bandwidth utilization is defined as μ = Bu/ Btot.
THE PROPOSED ADAPTIVE BANDWIDTH BORROWINGBASED CALL ADMISSION CONTROL SCHEME
Typically, a new or handoff call request would demand for its maximum bandwidth. While the CAC scheme would only guarantee the minimum bandwidth for admission or continued operation. In general, the CAC algorithm accepts a new or handoff call if the required bandwidth of the call satisfies the condition BWkreq ≤ Ba. In the following, we assume that class α calls belong to real time traffic with CBR (Constant Bit Rate), such as voice and video phone services. Class β calls belong to real time traffic with VBR (Variable Bit Rate), such as interactive multimedia and video streaming. While class γ calls belong to non real time traffic with VBR or UBR (Unspecified Bit Rate) such as email, remote login and file transfer services. In most cases, the bandwidth requirements for real time voice and video phone traffic are fixed. While the bandwidth requirements of multimedia applications are characterized to being tolerable and adaptable to transient fluctuations in the QoS. Therefore, the bandwidth requirements for classes β and γ may vary from their minimum to their required (maximum) bandwidth.
Call admission control (CAC) is a fundamental scheme for QoS provisioning in a network. It is a mechanism that restricts the access to the network based on the available resources in order to prevent network congestion and QoS deterioration.
A. New Calls Admittance Policy Generally, a new call of any class k is admitted if BWkreq ≤ Ba. When the new call is of class α, it is blocked if BWαreq > Ba. For class β or γ, a new call is also admitted if BWkmin ≤ Ba. The CAC algorithm would perform the required checks and blocks a new call in class β or γ, if BWkmin > Ba. In all cases, blocking a new call triggers a process of bandwidth borrowing from class β and γ active calls. The process consists of two parts: Bandwidth Borrowing Determination (BBD) and Bandwidth Rate Degradation (BRD). BRD is applied on all calls of classes β and γ, such that no bandwidth call is reduced Figure 1. New calls admittance policy.
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below its minimum. The amount of borrowed bandwidth from each class is proportional to the ratio of the amount of bandwidth allocated to the calls of this class to the total allocated bandwidth. The BRD algorithm is based on a MinMax fairness approach [11]. The flow chart in Figure 1 summarizes the new calls admittance policy. B. Handoff Calls Admittance Policy We adopt two approaches to reduce and control the handoff call dropping probability. The two approaches are based on a triggered process of incremental bandwidth borrowing. Both approaches depend on the priority of the traffic class of the incoming handoff call. In the first approach, a bandwidth rate degradation process may be applied incrementally on one or more β or γ active calls to satisfy the requirement of the arrived handoff call. In the second approach, sufficient bandwidth is collected by preempting one or more β or γ active calls. The preempted calls are queued for possible continuation of their communication later on. The amount of collected bandwidth by incremental rate degradation or preemption is called the borrowed bandwidth (Bb). The two approaches support higher priority to handoff calls over new calls, since a dropped handoff call is far less acceptable, from a user’s perspective, than a blocked new call. The adaptive CAC scheme accepts handoff calls of any class, given that the requested bandwidth BWkreq ≤ Ba. The CAC scheme differentiates between real time calls with CBR (i.e. class α) and calls of other classes (β and γ). A higher priority is given to class α handoff calls, followed by calls of class β, then calls of class γ. Incoming α handoff calls can borrow and/or preempt calls from class β and γ, starting from the lowest priority calls upward. However, incoming β handoff calls can borrow from class β and γ calls but can only preempt calls from class γ. On the other hand, incoming γ handoff calls can borrow from γ calls, but can not preempt any calls. Table I shows the relationship among the traffic classes for borrowing and preempting calls based on the prioritization scheme. The subscripts for each entry are used to illustrate that the incremental bandwidth borrowing and bandwidth call preemption are applied in increasing subscripts values. The handoff calls admittance policy is summarized by the flow chart shown in Figure 2. IV.
Figure 2. Handoff calls admittance policy
incoming calls. If the incoming call is originated from the same cell, it will activate the new call admittance policy, and if it is a handoff call it will activate the handoff call admittance policy. Generally, the new call admittance policy admits the incoming new call if there is an available bandwidth that satisfies the call; otherwise it will block the new call and activate Bandwidth Rate Degradation (BRD) procedure. However, the handoff call admittance policy admits the incoming handoff call if there is an available bandwidth that satisfies the call; otherwise it will activate Incremental Bandwidth Rate Degradation (IBRD) procedure to borrow the required bandwidth. If the procedure was not successful it will trigger the Call Preemption (CP) procedure to collect the required bandwidth. If the CP procedure was not successful, the handoff call will be dropped. These two policies were explained in section 3.
THE PROPOSED CALL ADMISSION CONTROL SYSTEM MODEL
The system model of the proposed Adaptive Bandwidth Borrowing-based (ABB) CAC scheme is presented in this section. The system model for the ABB scheme is shown in Figure 3. The model represents the configuration of the main components of the system in a base station. The CAC Manager is the main component in the CAC system. It organizes the communication between the other components and the communication system. In general, the CAC Manager keeps track of the available bandwidth, the incoming and outgoing calls, bandwidth returns and the rate of change in the available bandwidth. In addition, it activates the preempted calls from the preemption queue, when the system reaches a certain level of utilization. If there are no preempted calls in the preemption queue, the bandwidth rate upgrading procedure will be triggered to increase the currently allocated bandwidth of the activated calls. In case the CAC Manager receives a call from the requesting queue, it has mainly two methods to handle the
The communication system component represents a base station transmitter, receiver and a control unit. Each base station is characterized by its total bandwidth capability. The communication system control unit cooperates with the CAC manager for determining the amount of bandwidth to be allocated to an admitted call, and the amount of bandwidth to be degraded or upgraded for the currently active calls in the communication system. In other words, the CAC manager decides the amount of bandwidth to be assigned to the calls, and the communication system control unit assigns the amount of bandwidth determined by the CAC manager to the calls.
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Figure 3. System model of ABB CAC scheme.
Bandwidth Borrowing Determination (BBD) is a fundamental component of the system. It determines the amount of bandwidth needed to be borrowed from each traffic class based on the ratio of the bandwidth allocated to its calls. It is activated, when a new call is blocked, due to unavailable bandwidth for admitting the call. BBD monitors the history of the incoming and the outgoing calls, and the overall performance of the communication system. BBD uses the information collected from the history and the current utilization of the system to determine the amount of bandwidth needed to be borrowed without causing the bandwidth utilization to drop below the setup utilization threshold. The information collected is based on a measurement-based approach and the use of prediction metrics. It is assumed that the system performance is monitored periodically, in order to record the amounts of requested bandwidth by incoming calls and the returned bandwidth by outgoing or terminated calls, within each time interval in the cell.
the procedure will be terminated. The Bandwidth Rate Upgrading (BRU) component is based on the Max-Min fairness concept, using the water filling approach [11]. It is applied on each class of calls separately, according to each class workload. The objective is to distribute the amount of available bandwidth over all calls of a class fairly. This component will increase the current allocated bandwidth of the calls and accordingly would improve the utilization of the system. BRU will be activated when there is no preempted call in the preemption queue, and when the communication system utilization goes under the utilization threshold level by a certain factor. The Incremental Bandwidth Rate Degradation (IBRD) component is activated by the handoff call admittance process, when there is not enough bandwidth to admit the incoming handoff call. This component will follow the prioritization scheme mentioned in section 3.1. The IBRD main function is to find the required bandwidth to admit the call as fast as possible. The calls will not be degraded below their minimum bandwidth. If there is not enough bandwidth to borrow from the active calls, the call preemption component will be activated. Similarly, the Call Preemption component will follow the prioritization scheme mentioned in section 3.1. This component applies call preemption on one or more active calls incrementally, in decreasing order of their bandwidth, until the bandwidth needed to admit the call is satisfied. The component queues the preempted calls into the preemption queue (PQ), for
The Bandwidth Rate Degradation (BRD) component is based on the Min-Max fairness concept, using the water filling approach [11]. It is applied on each class of calls separately. The objective here is to distribute the amount of bandwidth to be borrowed fairly over all calls of the same class that can be borrowed from. The BRD component will borrow from each class fairly in a way that will not reduce the bandwidth of any call below its minimum required bandwidth. In addition, if there is not enough bandwidth to borrow from the active calls, TABLE II. Type
Traffic Class
Bandwidth Requirement
MULTIMEDIA TRAFFIC TYPES USED IN SIMULATION Average Bandwidth Requirement
Connection Duration 60–600 seconds 60–1800 seconds
Average Connection Duration
Example Voice Service & Audio-phone Video-phone & Video-conference
1
α
2
α
Real-Time (CBR) Real-Time (CBR)
3
β
Real-Time (VBR)
1–6 Mbps
3 Mbps
300–18000 seconds
600 seconds
Interact. Multimedia & Video on Demand
4
γ
Nonreal-Time (UBR)
5–20 kbps
10 kbps
10–120 seconds
30 seconds
Email, Paging & Fax
5
γ
Nonreal-Time (UBR)
64–512 kbps
256 kbps
30–36000 seconds
180 seconds
Remote Login Data on Demand
6
γ
Nonreal-Time (UBR)
1–10 Mbps
5Mbps
30–1200 seconds
120 seconds
File Transfer & Retrieval Service
30 kbps 256 kbps
378
180 seconds 300 seconds
&
SIMULATION PARAMETERS
Description Number of cells Cell radius Max. bandwidth capacity of cell Speed of mobile phone
1 0.95
Value 1 cell 200 m 50 Mbps 0–20 m/s
0.9 0.85 0.8
Utilization
TABLE III.
possible future reactivation. The Call Activation component is responsible of activating the preempted calls in the preemption queue. The call activation process is performed on calls that are still active and did not move out of the cell, when the bandwidth utilization drops below the setup utilization threshold by a certain level.
0.75 0.7 0.65 0.6 0.55 Scenario 1 Scenario 2 Scenario 3
0.5 0.45 0.4 1
2
3
4
5
6
7
8
9
10
Arrival Rate
V.
PERFORMANCE EVALUATION
Figure 4. Utilization vs. arrival rate.
The ABB CAC scheme has been implemented and evaluated through a simulation using OmNet++ simulation environment [15]. A discrete event simulation model has been developed for the scheme, using randomly generated calls, assuming a one cell (i.e., one BS) in a wireless cellular network. In this section, we specify the simulation parameters, and present selected results generated by the evaluation process, due to the limited available space. More results can be found in [12]. A discussion of the results is included, and a comparison with the results of some related works is given.
number of these cases are carefully selected and discussed in the coming paragraphs. In all the conducted tests, the ratios of α, β, and γ call types are set to 0.5, 0.25, and 0.25, respectively. Moreover, the ratios of new call and handoff call are set to 0.5, 0.5, respectively. In addition, the utilization threshold is fixed to 0.75. 1) Comparisons between the Scenarios The different scenarios are tested for utilization, probability of blocking and probability of dropping. The utilization vs. the arrival rate (μ vs. λ) chart is shown in Figure 4. It demonstrates that scenario 3 has the highest utilization. Scenario 2 has nearly equal utilization to scenario 3 at higher traffic rates. Since the available bandwidth is consumed very fast at high traffic rates; the reactivation and upgrading procedures would not have a chance to be triggered in scenario 3 as much as it would with lower traffic rates. The charts of the probability of blocking, Pb, and dropping, Pd, vs. the arrival rate, λ, are shown in Figures 5 and 6, respectively. Both charts show that scenario 1 has the highest Pb and Pd since it does not do more than accepting the call, if there is a sufficient bandwidth for the incoming call. Despite the fact that scenario 3 has the best bandwidth utilization, its blocking and dropping probabilities are slightly higher than scenario 2. Whenever calls are terminated or handed off in scenario 2, there will be more available bandwidth for the incoming calls, because there are no reactivation and upgrading procedures. While, in scenario 3, some of the available bandwidth will be consumed by the reactivation and upgrading procedures that will be activated, when the utilization drops below the setup threshold by a certain level. This situation increases slightly the chance of blocking and dropping of the incoming calls.
A. Simulation Parameters In this work, the representations of the multimedia traffic types are based on the call duration, the bandwidth requirement and the class of service. The classification strategy is adopted from [1]. The different application groups include real time as constant bit rate (CBR), real time as variable bit rate (VBR), and non real time data traffic sources as unspecified bit rate (UBR). The highest priority class is for real time (CBR) traffic. The next priority class is real time (VBR) traffic. The non real time (UBR) is the lowest priority class. Six types of traffic are carefully selected for the simulation. The respective parameter values of the traffic classes are shown in Table II. The uniform distribution between the minimum and maximum values within a given average is assumed to be followed by the call duration and bandwidth, as specified in Table II. The values closely represent realistic conditions, and were used by many researches [1], [9], [14]. Call requests are generated randomly based on a Poisson process with a mean arrival rate of λ (calls/second) in a cell. The parameters characterizing a generated call are randomly generated, such as its type, class, location and speed. We assume that a call can appear anywhere in the cell with an equal probability. The location and speed of a MT are used to determine the estimated initial duration of a call (i.e., call holding time), and the initial duration of a MT existing in the cell (i.e., call residency time), in order to simulate the handoff process. The parameter values used in the simulation are shown in Table III.
2) The proposed CAC scheme compared to related works Lu and Bigham [9] proposed an idea that is described briefly in section 2. They used the same traffic classes as in Table II. Their simulation model consists of 36 cells, and each cell has a bandwidth capacity of 30 Mbps. They used a Poisson distribution for the arrival rate in the range 0 to 1 call per second. While in this work, the arrival rate is from 1 to 10 calls per second. The obtained Pb in their research was between 0.35 and 0.4 at λ = 1. While Pd was slightly below 0.15 at λ = 1. However, in this study the Pb and Pd are 0.09 and 0.03, respectively, at λ = 1.
B. Results and Discussion Different scenarios were created to evaluate the performance of the proposed CAC. Scenario 1 is a nonadaptive scheme that only admits the calls if there is an available bandwidth, otherwise, the calls are blocked or dropped. Scenario 2 is an adaptive scheme without reactivation and upgrading procedures. Scenario 3 is the proposed ABB adaptive CAC scheme with all the procedures included. A
The general idea of the proposed scheme by Chang and Chen [1] was described in section 2. They used the same traffic classes as in this work. Their simulation environment is composed of 100 cells with a bandwidth capacity of 30 Mbps. The arrival rate followed a Poisson distribution in the range of
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0.45
0.4
0.4
0.35
0.35 Probability of Dropping
Probability of Blocking
0.45
0.3 Scenario 1
0.25
Scenario 2 0.2
Scenario 3
0.15
0.1
0 2
3
4
5
6
7
8
9
10
1
[6]
[7]
[8]
[9]
CONCLUSION
[10]
The contribution of this work is that it has applied an adaptive CAC scheme with complete bandwidth sharing successfully, using bandwidth borrowing and call preemption. The proposed ABB CAC scheme has been developed to deal with CAC problems and to provide QoS guarantees for multiclass traffic services. The simulation results show that the proposed CAC scheme, using fair bandwidth adaptation techniques, reduces the blocking probability and dropping probability, and improves the bandwidth utilization significantly. In addition, the simulation results have shown that some parameters, such as the utilization threshold and the allowed ratios of traffic classes, can improve the performance of the scheme if they are carefully tuned to control the level of the provided QoS.
[11]
[12]
[13]
[14]
For future work, the proposed adaptive CAC scheme can be simulated for a wireless cellular network of many cells. Wherein, an extended representation of physical mobility can be applied using mobility prediction techniques.
[15]
REFERENCES
[5]
3
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Figure 6. Probability of dropping vs. arrival rate
0.01 to 1 call per second. The bandwidth utilization in their research was above 90% at λ = 1. Though, in this study the bandwidth utilization is 80% at λ = 1 and bandwidth capacity of 50 Mbps. The call blocking probability obtained in their research was higher than 0.2 at λ = 1. While the dropping probability was 0.01 at λ = 1. However, in this study the Pb and Pd are 0.09 and 0.03, respectively, at λ = 1. Furthermore, the ABB CAC scheme has achieved in this study a bandwidth utilization of 96% with a Pb and Pd of 0.18 and 0.12 respectively, at λ = 10. Hence, the comparisons of our scheme with related scheme show the effectiveness and strength of the ABB scheme in its adaptation with high traffic loads. VI.
2
Arrival Rate
Figure 5. Probability of blocking vs. arrival rate
[4]
Scenario 3
0.15
0.05
Arrival Rate
[3]
Scenario 2
0.1
1
[2]
Scenario 1
0.2
0.05 0
[1]
0.3 0.25
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