WIRELESS/MOBILE networking is one of the strongest

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1 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY An Integrated Adaptive Bandwidth-Management Framework for QoS-Sensitive Multimedia Cellular Networks Sungwook Kim and Pramod K. Varshney, Fellow, IEEE Abstract Bandwidth is an extremely valuable and scarce resource in a wireless network. Therefore, efficient bandwidth management is necessary in order to provide high-quality service to users in a multimedia wireless/mobile network. In this paper, we propose new online bandwidth-management algorithms for bandwidth reservation, call admission, bandwidth migration, and call-preemption strategies. These techniques are combined in an integrated framework that is able to balance the traffic load among cells accommodating heterogeneous multimedia services while ensuring efficient bandwidth utilization. In addition, our online framework to adaptively control bandwidth is a cell-oriented approach that has low complexity, which makes it practical for real cellular networks. Simulation results indicate the superior performance of our bandwidth-management framework to strike the appropriate performance balance between contradictory quality-of-service requirements. Index Terms Bandwidth reservation, call admission, load balancing, multimedia cellular networks, online decisions, optimization, quality of service (QoS). I. INTRODUCTION WIRELESS/MOBILE networking is one of the strongest growth areas of communication technology today. In view of the remarkable growth in the number of users and the limited bandwidth allocated to this service, efficient management of bandwidth among the users becomes a key factor in enhancing network performance. Also, next-generation cellular mobile networks are expected to support multimedia services such as voice, video, data, etc. Different multimedia services over networks not only require different amounts of bandwidth, but also have different quality-of-service (QoS) requirements. Therefore, these services can be categorized into two classes: class I (real-time data) and class II (nonreal-time data) according to the required QoS [1] [3]. A. Scope of This Paper The challenge for next-generation multimedia cellular networks is to come up with bandwidth-management algorithms that are adaptable and efficient for varying traffic conditions and to accommodate as many heterogeneous multimedia traffic services as possible while ensuring QoS guarantees. Currently, Manuscript received October 15, 2002; revised April 9, 2003, December 18, 2003, and January 11, The authors are with the Electrical Engineering and Computer Science Department, Syracuse University, Syracuse, NY USA ( skim@ecs.syr.edu; varshney@ecs.syr.edu). Digital Object Identifier /TVT intensive research is being carried out on bandwidth-reservation and call-admission schemes in multimedia cellular networks. These schemes distinguish between new and handoff calls and give higher priority to handoff calls in order to guarantee a call s continuity. Call-admission control (CAC) restricts the number of admitted mobile users in order to provide the required QoS. This is done by blocking the new-call setup requests when the available bandwidth has reached the specified threshold [1], [2], [4]. During the operation of cellular networks, unexpected growth of traffic may occur in a specific cell. When the current traffic load in a cell is substantially larger than the design load, it may create local traffic congestion in cellular networks. In order to alleviate this kind of traffic overload condition, bandwidth migration can be employed. Specifically, this strategy can achieve efficient bandwidth utilization when the call-request rate fluctuates in each cell [5], [6]. Control decisions made at an earlier time may not be effective when network conditions change, which causes lower network performance and leads us to the possibility of preemption [7], [8] of existing calls considering current network conditions. Motivated by the above discussion, we propose new adaptive bandwidth-management algorithms that are suitable for multimedia cellular networks. Our algorithms are combined in an integrated framework in order to provide QoS guarantees. This holistic framework consists of intracell and intercell bandwidth-management schemes. The intracell bandwidth-management scheme partitions available bandwidth and reserves some parts are for higher priority handoff traffic services. If the reserved bandwidth is not sufficient, we allow our scheme to use bandwidth from the other reserved bandwidth pools. There exist optimal reservation amounts of bandwidths corresponding to the current traffic load, but these parameters vary with time. To attain the optimal amounts, we define traffic windows, which keep handoff and new-call traffic histories of each cell. By using these traffic windows, we can effectively adjust the size of the reserved bandwidth pools, which is more responsive to current traffic conditions of each cell. An intercell bandwidth-management scheme migrates bandwidth for load balancing. Our bandwidth-migration algorithm tries to minimize the maximum available bandwidth at each cell in order to approach perfect load balancing. When bandwidth is migrated in cellular networks, the corresponding bandwidths in the neighbor clusters, within reuse distance, have to be locked. Therefore, locking overhead is an important aspect of bandwidth migration and its minimization is critical for the success of bandwidth-migration schemes. In this paper, we select suitable bandwidth lender cells based on the cell /04$ IEEE

2 836 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 status classified by bandwidth reservations. Also, our bandwidth-migration rules carefully control bandwidth borrowing by considering the traffic situation over the entire network. Therefore, our approach for effective bandwidth management via load balancing enhances the performance of the entire cellular network. Earlier work reported in [1] and [2] has also considered adaptive bandwidth management. The adaptive bandwidth reservation (ABR) scheme [1] determines bandwidth reservation by using the largest of all the requested bandwidths from adjacent cells. As network conditions change after reservation, the reserved bandwidth is dynamically adjusted in each cell by an adaptive algorithm. The CAC provision scheme [2] predicts user mobility based on the information regarding user s position associated with each neighboring cell. If the user is in the overlap area defined as the departing region, bandwidth reservation is initiated in the neighboring cells. If these reservations are not successful, bandwidth is borrowed from one of their neighboring cells. However, these existing schemes have several shortcomings, as described in Section III. Compared to these schemes, our online algorithms attain better performance for QoS-sensitive multimedia services. B. Adaptive Online Approach The traffic patterns and future arrival rate of requests is generally not known. Furthermore, the fact that traffic patterns can vary dramatically over short periods of time makes the problem more challenging. Therefore, control decisions have to be made in real time and without the knowledge of future traffic requests at the decision time. Online algorithms [9] are natural candidates for the design of bandwidth-control schemes in multimedia cellular networks. Optimal offline algorithms are unrealizable for network management because it needs full knowledge of the future for an online problem. Our proposed online bandwidth-management algorithms, designed to handle bandwidth management based on real-time measurements of current network conditions, do not require advance knowledge or prediction regarding individual calls requests and future traffic mobility in cellular networks. In addition, for efficient network management, our algorithms use information dynamically to perform and adjust control decisions. Due to the uncertain network environment, this adaptive online strategy can significantly improve network performance while ensuring QoS for higher priority traffic services. Under various QoS constraints and a dynamically changing network traffic environment, it is almost impossible that a single strategy will achieve optimal performance over all traffic conditions. Therefore, it is necessary that various resource management algorithms cooperate with each other. Our algorithms are incorporated in an integrated framework in order to try to strike an appropriate performance balance between contradictory requirements while ensuring QoS guarantees. In this paper, our attempt is to make decisions in real time for ultimate practical implementation while trying to approximate the optimal network performance by adaptive online controls. Therefore, our online decisions are mutually dependent on each other under widely different and diversified network traffic situations. In addition, the proposed algorithms use dynamic information and history to adjust previously made decisions and system parameter values. Thus, our bandwidth-management framework has an ability to provide more efficient control over network condition fluctuations. This integrated online approach, which gives adaptability and flexibility to solve wide range of control tasks and well-balanced network performance, is a basic concept for our proposed frameworks. C. Contributions of Our Proposed Framework The important features of our proposed framework are: 1) management for heterogeneous multimedia services while guaranteeing QoS for higher priority traffic; 2) integrated framework with various cooperating bandwidth-management algorithms to maintain bandwidth efficiency as high as possible; 3) ability to respond to current network traffic conditions for the QoS-sensitive multimedia services; 4) well-balanced network performance between contradictory QoS requirements; 5) low complexity of decision mechanisms to make it practical for real network operations; 6) ability to achieve load balancing for cellular networks. The main challenge of our algorithm design is to try to strike the appropriate performance balance between contradictory requirements, e.g., ensuring higher bandwidth utilization, network throughput, and lower call-dropping and blocking probabilities under uniform and nonuniform traffic load distributions in the mobile cellular network. This paper is organized as follows. Section II describes the proposed framework in detail. In Section III, performance evaluation results are presented along with comparisons with the schemes proposed in [1] and [2]. Finally, concluding remarks are given in Section IV. II. BANDWIDTH-MANAGEMENT STRATEGIES Our bandwidth-management algorithms are designed for cellular mobile networks comprising a number of cells. Each cell is serviced by a base station (BS), which communicates with the users through wireless links. There is a node called the mobile switching center (MSC) that works as a gateway to and from the wide area network. An MSC is responsible for a set of cells, called a cluster. Each cell in a cluster is assigned a different bandwidth [2], [4] [6]. BS are responsible for intracell bandwidth control, while the MSC is responsible for intercell load management. Recent advances in wireless communication technologies have made it possible to provide heterogeneous multimedia services. However, multimedia traffic makes the problem more complex, since each call requires different bandwidth assignment and has a different priority according to the required QoS [1] [3]. In our scheme, three groups of traffic service are assumed: handoff traffic services (group I), new-call traffic services (group II), and traffic services requested by other cells for load balancing (group III). Also, each traffic group can be categorized into two classes: class I (real-time data) and class II (nonreal-time data).

3 KIM AND VARSHNEY: INTEGRATED ADAPTIVE BANDWIDTH-MANAGEMENT FRAMEWORK 837 A. Bandwidth-Reservation Strategy When a user moves in a cellular mobile network while a call is in progress, one of the main problems is handoff. To reduce the call-dropping rate, bandwidth reservation is a well-known and effective technique. However, reservation strategy allows trading off between the desired QoS for handoff services and bandwidth utilization. Therefore,the development of an efficient handoff support scheme while achieving high bandwidth utilization is one of the critical issues for the design of a cellular network. In this paper, we propose an online adaptive bandwidth-reservation algorithm. Our algorithm can dynamically adjust the amount of the reserved bandwidth based on the current network conditions. For adaptive online reservation adjustment, our algorithm provides a coordination paradigm by employing two different components event- and time-driven components. When the traffic is uniformly distributed over the entire cellular network and mobility is relatively uniform, the amount of handin and handout traffics in each cell are close to equal. Therefore, released bandwidths by handout services are enough to support handin services in each cell. For this ideal case, the event driven component to control the amount of reserved bandwidth is triggered by events handoff and new-call requests and call terminations. In our algorithm, bandwidth can be shared conditionally by different priority, which is essential to compromise a tradeoff between the desired QoS for handoff services and the high-bandwidth utilization. To provide QoS guarantees if the reserved bandwidth for higher priority services is not sufficient, our scheme allows the use of the bandwidth from the unused bandwidth pool for lower priority services. And, for bandwidth efficiency, if bandwidth is overreserved for higher priority services, the overreserved bandwidth is released for lower priority services. However, if traffic distributions and mobility is nonuniform, the event-driven component cannot catch up with the temporal and spatial traffic fluctuations. To overcome this limitation, a time-driven component is developed. The time-driven component controls the amount of reserved bandwidth via actions at regular time intervals. At the time of temporary traffic fluctuations, the reservation amount should be heavily influenced by current traffic changes. The main idea of the time-driven component is to adjust the amount of reserved bandwidth based on both the traffic history and recent traffic changes in each cell. These two components are executed at each BS in a distributed manner in order to guarantee QoS while ensuring bandwidth efficiency. 1) Event-Driven Component for Bandwidth Reservation: This component is triggered by the arrival of handoff and new calls. We further divide the handoff traffic (group I) services into two classes by data type: class I and class II. Correspondingly, we also divide the reserved bandwidth for group I traffic services into two parts. We denote the reserved bandwidth pools as for class I handoff traffic and for class II handoff traffic. The optimal amounts of bandwidths in the two reservation pools ( and ) are determined by the traffic load present at that time. To attain the optimal amounts at any given time, our proposed scheme adjusts the size of the two reserved bandwidth pools that reflect the current traffic conditions of each cell. For this online control, we define two traffic windows (which keep traffic histories based on real-time measurements) in each cell: one for class I traffic and the other for class II traffic. By using these traffic windows, the amount of the reserved bandwidth becomes dynamically responsive to current network conditions. The size of the traffic window is [ (or ), ], where is the current time and (or ) is the window length for class I (or class II) traffic. These window sizes are adjusted adaptively based on our online time-driven component discussed later. By using these traffic windows, we define and adjust the expected reservation bandwidth for handoff services. This value is determined by using the requested handoff rates from adjacent cells during the two traffic windows. is the sum of requested handoff bandwidths of class I traffic and is the sum of requested handoff bandwidths of class II traffic during the two respective traffic windows. and are obtained as and (1) where, are the number of handoff requests and the corresponding class I (class II) bandwidths of -type multimedia traffic service, respectively. Total expected reservation bandwidth is calculated as. The main steps of the event-driven component for bandwidth reservation are given next. At the initial time, the BS of a cell does not have any reserved bandwidth. When a mobile user comes from one of the neighboring cells (handoff call) or a new user requests bandwidth (new call), the BS of the current cell allocates bandwidth for this service from the unused bandwidth. When the bandwidth allocated to the handoff service is released by the termination of a call, this bandwidth is added to the reservation pool ( or ) of the appropriate class for future handoff services. When the next handoff request of class I arrives, the BS tries to admit this request by using the reserved bandwidth pool of class I. If sufficient bandwidth is not available in that reservation pool, unused bandwidth is used to satisfy this handoff service request. If unused bandwidth is not available to satisfy this request, then bandwidth from the reserved bandwidth pool for class II is used to satisfy this request if possible. This allocated bandwidth is added to the reserved bandwidth pool of class I at the time that this call terminates. When the next handoff request of class II arrives, the BS tries to admit this request by using the reserved bandwidth pool of class II. If sufficient bandwidth is not available in that reservation pool, unused bandwidth is used to satisfy this handoff service request. This allocated bandwidth is added to the reserved bandwidth pool of class II at the time that this call terminates. If the available bandwidth is not sufficient to provide the handoff service, the handoff call is dropped. When a new-call request arrives, the BS tries to admit this request by using the unused bandwidth. If the unused bandwidth is not sufficient for the new-call request,

4 838 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 Fig. 1. Traffic windows for expected reservation bandwidth. our scheme checks the class II reservation bandwidth pool. If the reserved bandwidth is larger than the expected reserved bandwidth over reserved bandwidth is released for the new-call request. If the released bandwidth is not sufficient to satisfy the new service request, our scheme checks the class I reservation bandwidth pool. If the reserved bandwidth is larger than the expected reserved bandwidth, over reserved bandwidth is released for the new-call request. If the available bandwidth is not sufficient to provide the new-call service, the new call is blocked. 2) Time-Driven Component for Bandwidth Reservation: In order to implement the time-driven component for bandwidth reservation, we partition the time axis into equal intervals of length. Ideally, we want the amount of reserved bandwidth to be equal to the requested handoff bandwidth during the next. In the time-driven component, the amount of bandwidth reservation is examined periodically, every, in order to maintain the reserved bandwidth close to the optimal value. This will be the optimal value in the sense of giving the smallest new connection-blocking probability while supporting all handoff traffic services. The value of is chosen based on desired system performance objectives. If is relatively small, which enables the algorithm to react more quickly and accurately to the changing traffics, system performance is more nearly optimal at the expense of control overhead. For large values of, the control overhead is less, but at the expense of system inefficiency; it could be too slow in adapting to real traffic changes (refer to Section III regarding the value of ). In our scheme, the traffic window size is adjusted adaptively based on the call-dropping probabilities of each cell. If the class I (or class II) handoff-dropping probability is larger than its predefined target probability (or ) based on QoS requirements, the corresponding traffic window size is increased. If the dropping probability is lower than the target probability, the size of the corresponding traffic window is decreased. Therefore, as network conditions change after reservation, our proposed scheme dynamically adapts the amount of the expected reserved bandwidth ( and ), as shown in Fig. 1. In addition to handling relatively homogeneous traffic conditions, our algorithm should also be responsive to traffic fluctuations in cellular networks. Since exact information regarding traffic in the future is not known, we are not able to determine the optimal amount of reservation bandwidth exactly. Therefore, we estimate its value based on traffic history and recent traffic changes in each cell. Our algorithm adjusts the reservation amount for both class I and class II traffics as a weighted average of two quantities as where is the amount of requested bandwidth during the interval, is the amount of reserved bandwidth at the current time, and is the amount of our optimized reserved bandwidth for the interval. (2)

5 KIM AND VARSHNEY: INTEGRATED ADAPTIVE BANDWIDTH-MANAGEMENT FRAMEWORK 839 The parameter controls the relative weights given to recent and past traffic histories in our decision. Under diverse traffic environments, a fixed value cannot effectively adapt to the changing traffic conditions. Therefore, based on the current network traffic conditions, our online algorithm dynamically modifies value each period. The value of is controlled in the same manner as the traffic window size. It is adjusted adaptively based on the call-dropping probabilities of each cell. If the handoff-dropping probability for class I or class II traffic for a cell is larger than its predefined target probability ( or ), it means that the current handin traffic that is coming in is heavier than can be handled by the reserved bandwidth, so the value of should be decreased. If the dropping probability is lower than what can be handled by the target probability, it means that the handin traffic is lighter than the reserved bandwidth, so the value of should be increased. Therefore, in our algorithm, each cell dynamically adjusts the value of to make the system more responsive to current traffic conditions in cellular networks. The time-driven component for window size adjustment and the determination of optimized reserved bandwidth is given next. Every, each BS monitors class I and class II call-dropping probabilities and then adjusts the traffic window sizes and the values of based on call-dropping probabilities. Traffic window sizes are defined as integer multiples of and the values of are multiples of. We set in this paper. If dropping probabilities are higher (lower) than the target probabilities ( and ), traffic window sizes are increased (decreased) in steps equal to. At the same time, the values of are decreased (increased) in steps equal to. Based on the available traffic information regarding each cell, BS, in a distributed manner, optimize the amount of reservation bandwidth for each cell according to (2). B. Load-Balancing Strategy The term load balancing has different (but related) meaning in different research areas. In wireless cellular networks, the common meaning of load balancing is to ease out the heavy traffic load in a specific cell due to a sudden traffic burst by borrowing bandwidth from other cells [5], [6]. Therefore, this strategy is expected to be efficient when the traffic in the cellular network is nonuniform due to temporal and spatial fluctuations. In the integrated framework presented in this paper, the main goal of load balancing is to balance the amount of available bandwidth among cells. Our load-balancing algorithm tries to minimize the maximum available bandwidth among cells in a cluster, so we can approach almost perfect load balancing in cellular networks. For the purpose of efficient load balancing, each cell is evaluated in terms of the amount of its available bandwidth. The available bandwidth of each cell is defined as its degree of availability. The average value of in a cluster is computed at the MSC based on the information collected from the cells in that cluster. Let us define a parameter called the average degree of availability of a cluster, which is computed as the arithmetic mean of each cell in the cluster [see (3) at the bottom of the page]. Our load-balancing algorithm is designed so that a heavily loaded cell that is in need of bandwidth migration tries to achieve the amount of available bandwidth equal to the of its own cluster. Therefore, the amount of bandwidth needed by a heavily loaded cell can be estimated as, where corresponds to the heavily loaded cell. In this paper, the amount of requested bandwidth is specified in terms of basic bandwidth units (BBUs), where one BBU is the minimum amount (e.g., 512 Kb in our system) of bandwidth migration. Within each cluster, our algorithm migrates a number of BBUs from suitable lender cells in a distributed manner. In this manner, we can reduce the migration overhead associated with selected lender cells. Let be the requested number of BBUs for load balancing, which is obtained as Recently proposed cellular-network control schemes classify cells into different categories [5], [6]. In [5], employing the number of available channels in the cell as a threshold, cells are classified into two categories. The performance was evaluated as a function of threshold values. In [6], two thresholds were used to classify the cells into three different classes. Heavy threshold ( ) is the average number of available channels in the cluster and light threshold ( ) is defined as. The value of determines the dispersion between light and heavy thresholds. Performance results were obtained as a function of. In this paper, our load-balancing algorithm develops cell-classification methods based on the traffic history of each cell. The cell status is determined in a highly adaptive manner that is responsive to a current traffic situation. This information is used for efficient load balancing in the multimedia cellular network. In the previous subsection, we developed an online bandwidth-reservation scheme for handoff traffic only. Here, we reserve bandwidth for the new-call traffic (group II), also in the same manner as the previous subsection. We define minimum and maximum bandwidth reservation amounts based on the current traffic condition of each cell. Based on the reservation amount, we categorize the cells into three classes with different status: peak (P) status, potential_peak (PP) status, and safe (S) status. In a P status cell (P cell), the reservation bandwidth for group II services is less than. Therefore, the traffic load of P cells is very heavy in that the total available bandwidth has reached a critically low point. When a cell reaches the P status, bandwidth migration is necessary for load balancing. A cell with PP status (PP cell) has reservation bandwidth for group II traffic services between and. A PP cell is not allowed (4) (3)

6 840 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 Fig. 2. Traffic window for P and PP estimation. to participate in any bandwidth migration neither to borrow bandwidth nor to lend bandwidth. Therefore, this cell status is in a neutral zone, which is used to prevent shuttling of cell status between P and S states. If a cell is neither a P cell nor a PP cell, it is a cell with S status (S cell). The S cells have enough available bandwidth so that a P cell can borrow bandwidth from S cells. For the determination of the reservation amounts and, we define a traffic window for group II traffic services. This window, in contrast to the traffic windows for group I traffic services in the previous section, is based only on new-call requests. The size of the traffic window to determine is, where is the current time and is the window length. The traffic window size is adjusted adaptively based on new-call-blocking probability. If the new-call-blocking probability is larger (smaller) than its predefined target probability, the corresponding traffic window size is increased (decreased). The value of can be estimated by using the traffic window, where is the current time. It is assumed that, as shown in Fig. 2. The parameters and of each cell are obtained based on the requested new-call rate during the traffic window as and where and are the number of new-call requests and the corresponding bandwidths of data type for group II traffic, respectively. The amount can be taken as the smallest value of reserved bandwidth to keep the new-call blocking probability (5) at a satisfactory level in each cell. By using these traffic windows, our scheme can adjust and and then adaptively classify the status of each cell based on current network conditions. When of a cell reaches below its own, the cell becomes a P cell, the BS of this cell requests bandwidth migration from the MSC. At the time of bandwidth migration for load balancing, in order to avoid bandwidth interference, the migrated bandwidth has to be locked not only in the lender cell of the current cluster, but also the same bandwidth in the cells within reuse distance. These cells, whose bandwidth interferes with migrated bandwidth, are called interference cells. Bandwidth locking degrades bandwidth utilization and network performance because locked bandwidth cannot be used to serve calls. Therefore, minimization of the locking overhead in cellular networks is an important issue when bandwidth migration is employed in bandwidth management [5], [6]. If an interference cell reaches P status due to the locking effect, extra bandwidth migration may become necessary, resulting in additional blocking in its neighboring clusters, giving rise to a catch 22 situation. At this time, due to this detrimental locking effect, the network performance degrades quite rapidly. In order to prevent this situation, our scheme controls the bandwidth-migration process by taking into account the status of interference cells also. We define the notion of, which is a group of cells that includes a candidate lender cell and its interference cells in neighboring clusters. There are as many s as the number of potential lender cells. The average of cells in the is defined as the total degree of availability. It is obtained as Our scheme selects the suitable lender cells based on. We want to find appropriate lender cells with a relatively high (6)

7 KIM AND VARSHNEY: INTEGRATED ADAPTIVE BANDWIDTH-MANAGEMENT FRAMEWORK 841 in the current cluster. The objective of our load-balancing algorithm is to minimize the maximum of each cluster. In addition to the concept of, we employ bandwidth-migration rules for the adaptive management of load balancing. At the decision time for bandwidth migration, these rules prevent, to the extent possible, propagation of detrimental locking effect for efficient bandwidth utilization. First, a candidate lender cell and all the interference cells in its should be S cells. Second, after lending or locking bandwidth for migration, they should continue to maintain their S status. Third, the cells in the selected are not permitted to borrow bandwidth from other cells. Therefore, if a cell in the selected becomes a P cell, our scheme provides the bandwidth-return procedure, instead of trying to migrate bandwidth. These migration rules attempt to prevent additional bandwidth migration in an effective manner and avoid the detrimental effect of locking. Bandwidth return is processed in the reverse order of the one followed for bandwidth borrowing. Until there is no more P cell in the selected, the borrower cell should return the borrowed BBUs back to its owner cell while unlocking the same bandwidths in interference cells. After the bandwidth return process, if the borrower cell is still a P cell, this cell again tries to migrate bandwidth from other suitable cells. This mechanism adaptively manages intercell bandwidth migration, taking into account the additional overhead of bandwidth borrowing, which makes the performance of the entire cellular network better. The bandwidth-migration-process algorithm for load balancing in cellular networks, which runs at the MSC, is outlined as follows. P cell in the cluster requests an MSC for bandwidth migration. MSC computes,, and using (3), (4), and (6). MSC sorts candidate lender cells in a decreasing order based on. Select cell having maximum value and check the appropriateness based on migration rules. If the migration rules are satisfied, one BBU is borrowed from the selected cell. Same bandwidths of interference cells in the selected are locked. Repeat the borrowing process until either the required number of BBUs ( ) have been borrowed or the available S cells in the cluster are exhausted. If a cell in the selected becomes a P cell, the borrower cell returns the borrowed BBUs to the lender cell while unlocking the same bandwidths in interference cells. After returning bandwidth, if the borrower cell status is P, it again requests bandwidth borrowing in the same manner as described above. When the required number of BBUs ( ) have been borrowed or the available S cells in the cluster are exhausted, the bandwidth-migration procedure terminates. C. Call-Preemption Strategy Because of the frequent and arbitrary movement of users in cellular networks, guaranteeing QoS for higher priority call services is a critical problem. Specifically, when the requested bandwidth for call services exceeds the available bandwidth, all higher priority services cannot be completely served. At this time, the problem of guaranteeing QoS is further intensified. Due to the online nature of bandwidth management, the decisions made previously to accept a new-call request may no longer be the right decision due to evolving traffic conditions. For example, the accepted call request at an earlier time may have used up the requested bandwidth in the cell, causing a subsequent higher priority call request to be rejected, which leads to lower network performance. Therefore, it would be beneficial to preempt the existing lower priority calls to satisfy the higher priority calls. Call preemption for communication networks has been considered previously in [7] and [8]. In order to adaptively handle call preemption, the issue is to decide which call connections can be preempted. This decision for call preemption has to be made in real time, without knowledge of future requests and their arrival statistics. Therefore, this decision problem is also categorized as an online computational problem. We propose a simple online call-preemption algorithm for enhancing network performance while ensuring better bandwidth utilization and keeping the QoS guarantees. We concentrate on specifying when and which call connection is to be preempted. At the time of the traffic overload situation (no available bandwidth for real time data handoff service), our call-preemption algorithm is triggered at the BS to reconsider the decisions made in the past. For deciding which calls to preempt, our scheme carefully analyzes the value of existing calls by considering the duration of a call thus far, allocated bandwidth, and its priority. At the call-preemption decision time, we will know the required bandwidth and priority of each connected call, which are given in advance, and the duration of the call thus far (from start time to current time). However, it is not possible to predict the total call-duration time (from start time to end time). Our scheme only considers current available information about the calls and does not employ any prediction. The call-transmission rate, employed as the main performance metric in our preemption scheme, is defined as the integral over time of the bandwidth used by a call. The transmission rate of a call, denoted by, is given by where is the required bandwidth of a call and and are the start and current times of call, respectively. The time period is the current duration of a call. Once a call is completed neither rejected nor preempted the total transmission rate of that call is considered to be gained by network throughput. Network throughput is the ratio of traffic that is completed successfully to all requested traffic. No gain is accrued for preempted or terminated calls in the middle of call connections. Therefore, preempting a connected call has obvious disadvantages: the communication work that was done thus far is lost and is not accrued for the network throughput. Therefore, our call-preemption scheme decides to preempt a certain connected call only when this decision can improve the entire network performance. Here, we provide decision rules for call preemption. First, high-priority call (class I) connections are never preempted. These call connections are allowed to (7)

8 842 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 TABLE I SUMMARY OF EACH INDIVIDUAL BANDWIDTH-MANAGEMENT ALGORITHM is to combine both satisfying very tight time bound to make a decision in real time and trying to approximate the optimal network performance by adaptive online controls. In this section, we have presented a number of bandwidthmanagement algorithms. Each scheme, which has a different control mechanism but the same online approach, is a component of our integrated framework that works together. The role of each component is summarized in Table I. Our online decisions in each algorithm are mutually dependent on each other to resolve conflicting QoS criteria under widely different and diversified traffic load situations. Other existing schemes, developed to support specific QoS parameters, cannot offer such an attractive tradeoff. Our online approach greatly improves the performance balance in cellular networks under widely diverse traffic load situations. The main features of our proposed framework adaptability, flexibility, and responsiveness to current network conditions come from this online approach. preempt low-priority call (class II) connections. Second, at the decision time, an ongoing call with lower accrued transmission rate is not allowed to preempt an ongoing call with a higher accrued transmission rate. Our call-preemption rules try to maximize network throughput while supporting different priority multimedia services. From the viewpoint of users, call-dropping and blocking probabilities are critical criteria to evaluate QoS in cellular networks. However, from the service providers viewpoint, throughput is a very important factor in order to maximize their profit (bandwidth service charge) during the operation of networks. Therefore, maximizing the throughput is also one of the goals for bandwidth management [10]. Our online bandwidth-management scheme strives to satisfy both users and service providers while balancing network performance. The call-preemption algorithm for intracell bandwidth control, which runs at the BS, is outlined as follows. If bandwidth in the current cell is not sufficient to support a higher priority call (class I of group I traffic) in spite of the load-balancing strategy, the call-preemption algorithm is triggered. Select only class II connected calls in the current cell. Compute current of the selected calls by using (7). Sort these calls in an increasing order based on and select the call with minimum. If the amount of bandwidth, which is the sum of available bandwidth and occupied bandwidth by the selected call, is larger than the requested bandwidth, then preempt this ongoing call. If the decision is not to preempt, examine the next call in the list for possible preemption. If there are no other calls in the list, the requested call is dropped. D. Discussion In algorithms to solve problems, there is tradeoff between solution quality and computation time. Usually, the goal of offline algorithms is to get optimal solutions with exponential time complexity. Therefore, it is too time consuming to implement in a real-time situation. Our attempt in this paper III. PERFORMANCE EVALUATION In this section, we evaluate the performance of our proposed algorithms using a simulation model. Based on this simulation model, we compare the performance of our scheme with two other existing schemes [1], [2]. There are other performance analysis methods: theoretical or numerical analysis. However, these methods have to be limited in scope limited modeling possibility for dynamic behavior. Therefore, for complex and complicated algorithms, such as our proposed framework, no capability makes tractable the theoretical and numerical model without many simplifications, which cannot provide precise performance evaluation. In contrast to these methods, a simulation analysis allows more complex realistic modeling for one real-world system. Therefore, in this paper, we propose a simulation model for the performance evaluation of our online framework. The assumptions for our simulation study are as follows. Simulated system consists of seven clusters and each cluster consists of seven microcells. In the even traffic load situation, the arrival process for new-call requests is Poisson with rate (calls/s/cell), which is uniform in all the cells. The range of offered load was varied from 0 to 3.0. In the uneven traffic load situation, the arrival process for new-call requests is Poisson with rate (calls/s/cell), which is uniform in all the cells except for the hot cell, for which. The range of offered load for normal cells was varied from 0 to 3.0. Network performance measures are plotted as a function of the offered load per second per cell (calls/s/cell). Based on this assumption, in our simulation model is 1s. Capacity of each cell is and the diameter of a cell is 1 km. Mobiles can travel in one of six directions with an equal probability. We consider three cases of user velocity: fast speed (120 km/h), slow speed (40 km/h), and 0 speed (stationary). Velocity of each mobile user is randomly selected from the above three cases. Based on speed and cell diameter, we assume cell-residence time for each mobile call for a

9 KIM AND VARSHNEY: INTEGRATED ADAPTIVE BANDWIDTH-MANAGEMENT FRAMEWORK 843 TABLE II MULTIMEDIA TRAFFIC AND SYSTEM PARAMETERS USED IN THE SIMULATION EXPERIMENT fast-speed user, it is 30 s; for a slow-speed user, it is 90 s; and for a stationary user, it is the same as the call-duration time. In order to represent various multimedia data, eight different traffic types are assumed based on connection duration, bandwidth requirement, and required QoS. They are generated with equal probability. Durations of calls are exponentially distributed with different means for different multimedia traffic types. Performance measures obtained through simulation are bandwidth utilization, call-blocking probability (CBP) of new calls, call-dropping probability (CDP) of handoff calls, throughput, etc. These performance measures obtained on the basis of ten simulation runs are plotted as a function of the offered load (call-arrival rate). Table II shows the multimedia traffic types and system parameters used in the simulation. With multiple classes of traffic, each has different traffic characteristics its own requirements in terms of bandwidth, QoS guarantee, and call-connection time. For real cellular network circumstance and fair comparison, we used the traffic types, characteristics, and system parameters given in realistic simulation model [1] and [11] [13]. These values are carefully chosen to make the simulation feasible. In the ABR scheme [1], a class I call is not only allocated the requested bandwidth in its current cell, but the same amount of bandwidth is reserved in all of its neighboring cells. If the allocation or reservation fails, then the call request is rejected. For a class II call, the requested bandwidth is reserved only in the originating cell for the call. And, for a class I handoff service, bandwidth is reserved in the new neighboring cells of the cell into which the handoff connection is moving. At the same time, the reserved bandwidth in old neighbor cells is released. When bandwidth reservation succeeds in all new neighboring cells, the handoff connection is accepted. The CAC provision scheme [2] is an integrated approach that provides CAC, bandwidth reservation, and borrowing to guarantee QoS. In this scheme, the CAC is different for individual users depending on their position in the cell and the data type. The class I call in a cell is classified as either a local or departing call, depending on the location of a user in a cell. If a class I call user is in the departing region, bandwidth reservation is initiated in the neighboring cells. If the requested bandwidth is not available in any one of these neighboring cells, bandwidth is borrowed according to a borrowing function. If these reservations are successful, then the class I call is admitted. However, there are some drawbacks to these two schemes. In the scheme in [1], bandwidth is reserved redundantly because the user moves to only one of the neighboring cells and the stringent call-admission procedure might reject many class I call requests in a highly overloaded system. In addition, there is no facility to control traffic congestion. In the scheme in [2], the load-balancing technique can be considered only for class I handoff traffic services and they do not consider the detrimental locking effect of bandwidth borrowing. In addition, BS need to track each user s mobility to classify the user according to its position in a cell, which may cause heavy system overhead. A. Results for Even Traffic-Load Distribution For results presented in Fig. 3, the arrival process corresponding to new-call requests is uniform in all the cells and the traffic load in the multimedia cellular network is even. Fig. 3(a) shows bandwidth utilization for the three schemes. It is shown that, for low call-arrival rates, the bandwidth utilization is virtually the same as for the three schemes. However, as the call-arrival rate increases, the bandwidth utilization of our scheme is better than the other schemes. In the CAC provision

10 844 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 Fig. 3. Performance evaluation for uniform traffic situation. (a) Bandwidth utilization. (b) CBP. (c) CDP. (d) CBP (class I). (e) CDP (class I). (f) Throughput. (g) Call_complete probabilities. (h) Call_complete (class I) probabilities. Fig. 4. Performance evaluation for nonuniform traffic situation. (a) Bandwidth utilization. (b) CBP. (c) CDP. (d) CBP (class I). (e) CDP (class I). (f) Throughput. (g) Call_complete probabilities. (h) Call_complete (class I) probabilities. scheme, bandwidth is not utilized well due to the detrimental locking effect in the heavy-load traffic situation. Therefore, bandwidth utilization decreases substantially. Fig. 3(b) and (c) shows the performance comparison for all traffic services in terms of CBP and CDP. When the call-arrival rate is low (below 0.5), the performance of the three schemes is identical. This is because all three schemes have enough bandwidth to accept the requested calls. As the call-arrival rate increases, the average amount of unused bandwidth decreases. Thus, new-call requests are likely to be rejected and CBP increases, but the CDP of handoff calls quickly settles down due to bandwidth reservation. Since the scheme in [2] only reserves bandwidth for class I handoff calls, it has good CDP performance when only class I traffic is considered. Fig. 3(d) and (e) shows the CBP and CDP of real-time (class I) data traffic services. All three schemes have similar trends; however, due to the strict real-time data call control of the ABR scheme, CBP and CDP for real-time data increases rapidly in heavy-traffic-load situations. The curves indicate that our scheme and scheme in [2] improve the performance of class I traffic services (CBP and CDP) more significantly than the ABR scheme. In Fig. 3(f) (h), network throughput, call_complete, and class I (real-time data type) call_complete probability are presented. Our scheme performs better than both of the other schemes. From the simulation results we obtained, it can be seen that our scheme, in general, performs better than the two existing schemes from low to heavy traffic-load distributions. B. Results for Uneven Traffic-Load Distribution Fig. 4 shows the performance comparison for a different traffic scenario. Here, our model is extended to a nonhomogeneous traffic-load distribution in each cell different call-arrival rate in the cells. To model such a traffic situation,

11 KIM AND VARSHNEY: INTEGRATED ADAPTIVE BANDWIDTH-MANAGEMENT FRAMEWORK 845 Fig. 5. Performance evaluation in terms of weighted network efficiency. (a) Even traffic situation. (b) Uneven traffic situation. we assume two different types of cells in the cellular network: a hot cell, which has a heavy traffic intensity, and a normal cell with traffic intensity, which are the other cells in the cluster. When the traffic load is unbalanced, the load-balancing policy is expected to significantly influence the cellular network performance. Fig. 4(a) shows the bandwidth utilization for the three schemes. When the traffic intensity in normal cells is low, bandwidth utilization is fairly high due to our load-balancing policy. However, in order to control the locking effect adaptively, our scheme employs the bandwidth-return procedure. As the call-arrival rate in normal cells approaches, the advantage of load-balancing policy decreases. Therefore, the bandwidth utilization of our scheme, which is much better under light traffic load, gets closer to the ABR scheme. Our bandwidth-migration scheme can significantly increase the capacity in the congested cells, which can be used to support new-call services. Therefore, in Fig. 4(b), our scheme can attain excellent CBP under light traffic load in normal cells. As the call-arrival rate increases in normal cells, the CBP of our scheme increases and becomes similar to the other schemes. Fig. 4(c) shows the CDP for handoff calls; this result is also similar to CBP. For small, CDP of our scheme is much better than the other schemes. As increases, our scheme and the ABR scheme have similar trends they increase as a function of the arrival rate of new calls, but quickly settle down. The curves presented in Fig. 4(d) and (e) show that our scheme and the scheme in [2] improve the performance of class I traffic services (CBP and CDP) more significantly than the ABR scheme. In Fig. 4(f) (h), network throughput, call_complete, and class I (real-time data type) call_complete probability are presented. Due to the load-balancing policy, our scheme performs significantly better than both the schemes when the traffic intensity in normal cells is low. Our online bandwidth-management approach is quite adaptable to dynamic traffic that changes in a widely different and diversified manner. This feature is highly desirable to provide better network efficiency. More stringent management in order to reduce the CDP of class I in schemes [1] and [2] mandates more conservative bandwidth reservation. However, our proposed online algorithms attempt to adaptively reserve only the amount of bandwidth that can balance other performance criteria. From the simulation results, it is clear that our integrated scheme, in general, achieves superior performance for varying traffic load conditions in cellular networks. Specifically, our integrated framework balances appropriate performance between contradictory requirements better than the approaches proposed in [1] and [2]. C. Results for Overall Network Efficiency In multimedia cellular networks, bandwidth is shared by traffic of different priorities. Therefore, bandwidth management is a very complex problem and a number of performance tradeoffs need to be considered. For example, to provide better performance for higher priority handoff services (low CDP) by reserving bandwidth, we need to sacrifice bandwidth utilization and CBP. If we try to improve bandwidth utilization and CBP, CDP will degrade. Similarly, bandwidth borrowing for load balancing will result in decreased bandwidth-reuse efficiency in cellular networks and, ultimately, in degraded total network capacity. Our holistic framework attempts to consider all these tradeoffs and provides a practical real-time solution that resolves the conflicting system requirements. As we mentioned earlier, it is important to obtain a balanced network performance considering all of the parameters. Therefore, in order to evaluate total network performance based on these tradeoffs, we define weighted network efficiency as a performance metric [14] and study the performance of different algorithms based on this metric. Let be the new-call acceptance probability, be the handoff acceptance probability, be the bandwidth utilization, be the call-complete probability, be the real-time (class I) data call-complete probability, and be the throughput. Let,,,,, and be the relative weights for

12 846 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 53, NO. 3, MAY 2004 the six performance measures. Usually, we give higher weights for class I calls and handoff traffic services. Here, we define the total network efficiency based on the weights,,,,, and as Figs. 5(a) and (b) show the weighted network efficiency for varying load conditions even and uneven traffic distributions. Our integrated scheme can better control the bandwidth to support a balanced network performance in each cell in cellular networks. Specifically, more traffic is carried in the asymmetric case when our scheme is applied. Simulation results clearly indicate that, from low to heavy traffic load intensities, our framework maintains well-balanced network performance between conflicting QoS parameters. In Fig. 5(b), due to the load-balancing technique, we can see the significant performance gain of our framework in low-traffic-load intensities. In addition, under heavy traffic intensities, our framework does not sacrifice the network s performance. This means that, without performance tradeoff between network adaptability and bandwidth efficiency, we can maintain superior network performance under diversified network traffic-condition changes. IV. SUMMARY AND CONCLUSION In this paper, online adaptive bandwidth-management algorithms for multimedia cellular networks are proposed. In order to provide QoS-sensitive multimedia services, our integrated framework manages bandwidth by suitably combining various control techniques bandwidth reservation, call admission, bandwidth migration for load balancing, and call preemption. Our framework is able to resolve conflicting QoS criteria while ensuring efficient network performance. In addition, our online approach is cell oriented in that adaptation decisions are made on a cell-by-cell basis. Therefore, it has low complexity, making it practical for real wireless cellular networks. We compared the performance of our scheme with two existing schemes, namely the ABR and CAC provision schemes. Performance evaluation results indicate that our framework maintains a well-balanced network performance between contradictory QoS requirements in widely different and diversified traffic-load situations,while other schemes cannot offer such an attractive performance balance. REFERENCES [1] C. Oliveria, J. B. Kim, and T. Suda, An adaptive bandwidth reservation scheme for high-speed multimedia wireless networks, IEEE J. Select. Areas Commun., vol. 16, pp , Aug [2] R. Jayaram, S. K. Sen, N. K. Kakani, and S. K. Das, Call admission and control for quality-of-service (QoS) provisioning in next generation wireless networks, Wireless Networks, vol. 6, pp , [3] M. A. Marsan, S. Marano, C. Mastroianni, and M. Meo, Performance analysis of cellular mobile communication networks supporting multimedia services, Mobile Networks Applicat., vol. 5, no. 3, pp , [4] S.Sunghyun Choi and K. G.Kang G. Shin, Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks, in Proc. ACM SIGCOMM 98, Sept. 1998, pp [5] S. K. Das, S. K. Sen, and R. Jayaram, A dynamic load balancing strategy for channel assignment using selective borrowing in cellular mobile environment, Wireless Networks 3, pp , (8) [6] Y. Zhang and S. K. Das, An efficient load-balancing algorithm based on a two-threshold cell selection scheme in mobile cellular networks, Comput. Commun., vol. 23, pp , [7] A. Bar-Noy, R. Canettiz, S. Kuttenx, Y. Mansour, and B. Schieberz, Bandwidth allocation with preemption, SIAM J. Comput., vol. 28, no. 5, pp , [8] J. A. Garay and I. S. Gopal, Call preemption in communication networks, in Proc. INFOCOM 92, vol. 44, 1992, pp [9] A. Borodin and R. El-Yaniv, Online Computation and Competitive Analysis. Cambridge, U.K.: Cambridge Univ. Press, [10] P. Thomas, D. Teneketzis, and J. K. MacKie-Mason, A market-based approach to optimal resource allocation control in integrated-services connection-oriented networks, Dept. Elect. Eng. Comput. Sci., Univ. Michigan, Ann Arbor, [11] C. Oliviera, J. B. Kim, and T. Suda, Quality-of-service guarantee in multimedia wireless networks: a simulation study, in Proc. IEEE IN- FOCOM 96, 1996, pp [12] G. Gallassi, G. Rigolio, and L. Fratta, ATM: Bandwidth assignment and bandwidth enforcement policies, in Proc.IEEE GLOBECOM 89, Dallas, TX, Nov. 1989, pp [13] D. Raychaudhuri and N. D. Wilson, ATM-based transport architecture for multiservices wireless personal communications networks, IEEE J. Select. Areas Commun., vol. 12, pp , Oct [14] W. Su and M. Gerla, Bandwidth allocation strategies for wireless ATM networks using predictive reservation, in Proc. IEEE GLOBECOM 98, Nov. 1998, pp Sungwook Kim was born in Seoul, Korea, on June 13, He received the B.S. and M.S. degrees in computer science from Sogang University, Seoul, Korea, in 1993 and 1995, respectively. He received the Ph.D. degree in computer science from Syracuse University, Syracuse, NY, in From 1995 to 1998, he was a Member of Technical Staff at A. I. Soft. Company Ltd., Seoul, Korea. Since 1999, he has been with Syracuse University. His research interests include online algorithms, multimedia network management, bandwidth allocation, and adaptive QoS control. Pramod K. Varshney (M 77 SM 82 F 97) was born in Allahabad, India, on July 1, He received the B.S. degree in electrical engineering and computer science (with highest honors) and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois, Urbana-Champaign, in 1972, 1974, and 1976, respectively. From 1972 to 1976, he held teaching and research assistantships at the University of Illinois. Since 1976, he has been with Syracuse University, Syracuse, NY, where he is currently a Professor of Electrical Engineering and Computer Science. He served as the Associate Chairman of the department from 1993 to Presently, he is the Research Director of The New York State Center for Advanced Technology in Computer Applications and Software Engineering (CASE). He is the author of Distributed Detection and Data Fusion (New York: Springer-Verlag, 1997). His current research interests are in data and information fusion, wireless communications, signal and image processing, remote sensing, communication networks, and distributed and parallel algorithms for signal processing. Dr. Varshney was a James Scholar, a Bronze Tablet Senior, and a Fellow while at the University of Illinois. He is a member of Tau Beta Pi and is the Recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. In 2000, he received the Third Millenium Medal from the IEEE and the Chancellor s Citation for Exceptional Academic Achievement at Syracuse University. He was the Guest Editor of the PROCEEDINGS OF THE IEEE Special Issue on Data Fusion in January He is on the Editorial Board of Information Fusion and is a distinguished lecturer for the IEEE Aerospace and Electronic Systems Society. He was the President of the International Society of Information Fusion in 2001.