Adaptive Load Balancing with Preemption for ... - Semantic Scholar

Adaptive Load Balancing with Preemption for Multimedia Cellular Networks Sungwook Kim

Pramod K. Varshney

Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY 13244, USA [email protected]

Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY 13244, USA [email protected]

Abstract- 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 an on-line load balancing algorithm with preemption. This technique is able to balance the traffic load among cells accommodating heterogeneous multimedia services while ensuring efficient bandwidth utilization. The most important features of our algorithm are its adaptability, flexibility and responsiveness to current network conditions. In addition, our online scheme to control bandwidth adaptively is a cell-oriented approach. This approach has low complexity making it practical for real cellular networks. S imulation results indicate the superior performance of our algorithm. Keywords – Multimedia cellular networks, On-line decisions, Optimization, Load balancing, Quality of Service.

I. INTRODUCTION 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 [1,2,3]. During the operation of cellular networks, unexpected growth of traffic may occur in a specific cell. 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 [4,5]. Thus, the challenge for next generation cellular networks is to come up with techniques that are able to balance the traffic load among cells accommodating heterogeneous multimedia services while ensuring efficient bandwidth utilization. Motivated by the above discussion, we propose an adaptive load balancing algorithm suitable for multimedia cellular networks. In [7], we introduced an online bandwidth reservation concept for intra-cell bandwidth management in which the amount of the reserved bandwidth is dynamically adjusted based on the current network conditions. In this paper, we propose a methodology for inter-cell bandwidth management in which we try to minimize the maximum available bandwidth in each cell in order to approach perfect load balancing. For efficient bandwidth utilization, control decisions must be dynamically adjustable. These decisions

have to be made in real time, and also without the knowledge of future traffic requests at the decision time. Furthermore, the fact that traffic patterns can vary dramatically over short periods of time makes the problem more challenging. Therefore, online algorithms [6] are natural candidates for the design of efficient bandwidth control schemes in QoS sensitive multimedia cellular networks. Our online load balancing algorithm does not require advance knowledge or prediction of future traffic mobility. In addition, decisions are made on a cell-oriented basis. This approach has low complexity making it practical for real cellular networks. Online decisions made at an earlier time may not be effective when network conditions change. This situation causes lower network performance. This leads us to the possibility of preemption of existing calls considering current network conditions. The most important feature of our algorithm is its ability to respond to traffic conditions that may include both uniform and non-uniform traffic load distributions in the mobile cellular network. 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, higher network throughput, and lower call dropping and blocking probability. This paper is organized as follows. Section II describes the proposed scheme 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 Recent advances in wireless communication technologies have made it possible to provide heterogeneous multimedia services. However, the multimedia environment makes the problem more complex, since each call requires different bandwidth assignment and has a different priority according to the required QoS [1,2,3]. In our scheme, three groups of traffic service are assumed: hand off 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 (non-real time data).

A. Load Balancing Strategy In this paper, 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 (BA ). The average value of BA in a cluster is computed at the Mobile Switching Center (MSC) based on the information collected from the cells in that cluster. Let us define a parameter called the average degree of availability ( B avg ) of a cluster, which is A computed as the arithmetic mean of the BA of each cell in the cluster. Sum of degree of availability (BA ) of each cell in the cluster Bavg = A Number of cells in the cluster

(1)

Our load balancing algorithm is designed so that a heavily loaded cell needing bandwidth migration tries to achieve the amount of available bandwidth equal to the B avg of its own A cluster. Therefore, the amount of bandwidth needed by a heavily loaded cell, can be estimated as B avg - BA , where BA A 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 Kbps 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 X be the requested number of BBUs for load balancing, which is obtained as X = ( B avg - BA ) / BBU A

(2)

Recently proposed cellular network control schemes classify cells into different categories [4,5]. 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 manner that is responsive to current traffic situation. This information is used for efficient load balancing in the multimedia cellular network. In our previous work [7], we developed an online bandwidth reservation scheme for handoff traffic only. In this paper, we reserve bandwidth for new call traffic (group II) also in the same manner as in [7]. We define minimum ( MIN b ) and maximum ( MAX b ) reservation amounts based on the current traffic condition of each cell. By using these reservation amounts, we categorize the cells into three classes with different status: P (peak ) status, PP (potential_peak ) status and S (safe) status. In a P status cell (P-cell), the reservation

bandwidth for group II services is less than MIN b . Therefore, the traffic load of P-cells is very heavy in that the total available bandwidth has reached a critical 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 MIN b and MAX b . A PP-cell is not allowed 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 MIN b and MAX b , we define a traffic window for group II traffic services. The traffic window size is adjusted adaptively based on the online estimate of new call blocking probability. If the new call blocking probability is larger (smaller) than its predefined target probability (Ptarget ), the corresponding traffic window size is increased (decreased). The window size for MAX b ( MIN b ) is [t c - t win (unit_time),

t c ], where t c is the current time, t win is the window length and unit_time is the period of a periodic pattern used to examine the reservation status. By using this traffic window, the values of MAX b and MIN b can be estimated online via the sum of requested new call bandwidths of group II traffic as

MAXb =

å (B ´ N )

i Î Wgroup _II

i

i

and MINb =

å (B ´ N )

i ÎWunit _time

i

i

(3)

where Ni and Bi are the number of new call requests and the corresponding bandwidths of data type i for group II traffic, respectively. The amount MIN b can be taken as the smallest value of reserved bandwidth to keep the new call blocking probability at a satisfactory level in each cell. By using these MIN b and MAX b values, our scheme can classify the status of each cell based on current network conditions. When BA of a cell reaches below its own MIN b - the cell becomes a Pcell, the base station 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 can not 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 [4,5]. In order to prevent detrimental locking effect, our scheme controls the bandwidth migration process by taking into account the status of interference cells also. We define the notion of borrow_set which is a group of cells – that includes a candidate lender cell and its interference cells in neighboring clusters. There are as many borrow_sets as the number of potential lender cells. The average BA of cells in the borrow_set i (1 ≤ i ≤ total number of lender cells) is defined as the total degree of availability ( Ti avg ). It is obtained as Ti avg =

å (B

) / number of cells in borrow_ set i

A n n Îborrow_set i

• Repeat the borrowing process until either the required number of BBUs (X) have been borrowed or the available S cells in the cluster are exhausted. • If a cell in the selected borrow_set 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 requests bandwidth borrowing again in the same manner as described above. • When the required number of BBUs (X) have been borrowed or the available S cells in the cluster are exhausted, the bandwidth migration procedure terminates.

(4)

Our scheme selects the suitable lender cells based on Ti avg . We want to find appropriate lender cells with a relatively high Ti avg in the current cluster. The objective of our load balancing algorithm is to minimize the maximum Ti avg of each cluster. In addition to the concept of borrow_set, we employ bandwidth migration rules for the adaptive management of load balancing. First, a candidate lender cell and all the interference cells in its borrow_set should be Scells. Second, after lending or locking bandwidth for migration, they should continue to maintain their S status. Third, the cells in the selected borrow_set are not permitted to borrow bandwidth from other cells. Therefore, if a cell in the selected borrow_set becomes a P cell, instead of trying to migrate bandwidth, our scheme provides the bandwidth return procedure. These migration rules attempt to prevent additional bandwidth migration in an effective manner and avoid 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 borrow_set, 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 tries to migrate bandwidth again from other suitable cells. The bandwidth migration algorithm for load balancing in cellular networks, which runs at the MSC, is outlined below: • The P-cell in the cluster requests an MSC for bandwidth migration. • The MSC computes B avg , X and Ti avg using (1),(2),(4). A • The MSC sorts candidate lender cells in a decreasing order based on Ti avg . • Select cell having maximum Ti avg value and check the appropriateness based on migration rules. If the migration rules are satisfied, one BBU is borrowed from the selected cell. • The same bandwidths of interference cells in the selected borrow_set are locked.

B. Call Preemption Strategy Due to the on-line 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. Therefore, it would be beneficial to preempt the existing lower priority calls to satisfy the higher priority calls. 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 hand off 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 carefu lly analyzes the value of existing calls 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). Therefore, our scheme just 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 i, denoted by Ti , is given by

Ti where

=

tc ts

bi d t

(5)

bi is the required bandwidth of a call and t s , t c are

start and current time of call i, respectively. The time period

t c - t s is the 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. Here, we provide decision rules for call preemption. First, high priority call (class I) connections are never preempted. Instead, these call connections are allowed to preempt lowpriority call (class II) connections. Second, at the decision time, a higher priority call with lower accrued transmission rate is not allowed to preempt an existing call with 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 [8]. Our online bandwidth management scheme strives to satisfy both users and service providers while balancing network performance. The call preemption algorithm is outlined below • If bandwidth in the cell is not sufficient to support a higher priority call (class I of group I traffic) in spite of load balancing strategy, the call preemption algorithm is triggered. • Select only class II connected calls in the current cell. • Compute Ti of the selected calls by using (5). • Sort these calls in an increasing order based on Ti and select the call with minimum Ti . • 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.

III. SIMULATION EXPERIMENTS In this section, we employ a simulation model for the mobile environment to evaluate the performance of our bandwidth management strategy. The assumptions for our simulation study are as follows. • The simulated system consists of 7 clusters, and each cluster consists of 7 micro cells. • The arrival process for new call requests is Poisson with rate λ (calls/s/cell), which is uniform in all the cells except for the hot cell for which l h =3.0. The range of offered load for normal cells, l n , was varied from 0 to 3.0.

• Mobiles can travel in one of 6 directions with an equal probability. We consider three cases of user speed, fast speed (120 km/h), slow speed (40 km/h) and 0 speed (stationary). • The capacity of each cell is C (= 30Mbps), and the diameter of a cell is 1 km. • In order to represent various multimedia data, eight different traffic types are assumed based on connection duration, bandwidth requirement and required QoS. The 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 hand-off calls, throughput, etc. These performance measures are plotted as a function of the offered load (call arrival rate) l n . In previous research, the ABR scheme [1] and the CAC provision scheme [2] have been proposed for adaptive bandwidth management for multimedia cellular networks. There are several differences between these two schemes [1,2] and our approach. In the ABR scheme [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 the CAC provision scheme [2], the load balancing technique can be considered for class I handoff traffic services only and they do not consider detrimental locking effect of bandwidth borrowing. In addition, base stations trace each user’s mobility to classify the users position in a cell, which may cause heavy system overhead. In contrast to these schemes, we try to minimize bandwidth waste by reserving bandwidth based on the online measures of network conditions, and employ several adaptive bandwidth management rules. Figs. 1-3 show the performance comparison for a nonhomogeneous traffic load distribution among cells - different call arrival rates in the cells. When the traffic load is unbalanced, the load balancing policy is expected to significantly influence the cellular network performance. In Fig. 1, due to the borrowed bandwidth for load balancing, which increases the capacity in the congested cells, our scheme can attain excellent CBP under light traffic load in normal cells. As the call arrival rate l n increases in normal cells, CBP of our scheme increases similar to the other schemes. The CDP for hand-off calls is also similar to CBP. For small l n , CDP of our scheme is much better than the other schemes. The curves presented in Fig. 2 show that our scheme and the scheme in [2] improve the performance of class I traffic services (CBP and CDP) significantly than the ABR scheme. Specifically, due to the strict class I call admission, class I data CBP of the ABR scheme increases rapidly in heavy traffic load situation. Fig. 3 shows the bandwidth utilization and throughput for three schemes. When the traffic intensity in normal cells is low ( l n < 0.5),

0.7

1 Our proposed Scheme(CBP) ABR Scheme(CBP) CAC provision Scheme(CBP) Our proposed Scheme(CDP) ABR Scheme(CDP) CAC provision Scheme(CDP)

0.5

0.9 Bandwidth Utilization and throughput

Blocking & Dropping Probability

0.6

0.4

0.3

0.2

0.1

0.8 0.7 0.6 0.5 0.4 0.3

Our proposed Scheme(utilization) ABR Scheme(utilization) CAC provision Scheme(utilization) Our proposed Scheme(throughput) ABR Scheme(throughput) CAC provision Scheme(throughput)

0.2 0.1

0

0

0.5

1 1.5 2 Offered Load (Call Arrival Rate)

2.5

3

Figure 1. Call Blocking and Dropping Probability Performance

IV. SUMMARY AND CONCLUSIONS In this paper, online bandwidth management algorithms for multimedia cellular networks are proposed. Our proposed load balancing algorithm tries to minimize the maximum available bandwidth among cells in an adaptive manner based on current network traffic conditions. This approach is able to resolve conflicting performance criteria while 1 Our proposed Scheme(CBP) ABR Scheme(CBP) CAC provision Scheme(CBP) Our proposed Scheme(CDP) ABR Scheme(CDP) CAC provision Scheme(CDP)

Blocking & Dropping (class I) Probability

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.5

1 1.5 2 Offered Load (Call Arrival Rate)

2.5

0

0.5

1 1.5 2 Offered Load (Call Arrival Rate)

2.5

3

Figure 3. Bandwidth Utilization and throughput

bandwidth utilization is fairly high due to our load balancing policy. Our online bandwidth management approach is quite adaptable to dynamic traffic that changes in widely different and diversified manner. This feature is highly desirable to provide better network efficiency. Through performance comparisons, it is clear that our scheme achieves superior results for varying traffic load conditions in cellular networks. Specifically, our scheme balances appropriate performance between contradictory requirements better than the approaches proposed in [1] and [2].

0.9

0

3

Figure 2. Call (Class I) Blocking and Dropping probability Performance

ensuring efficient network performance. In addition, our scheme 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 networks. We compared the performance of our scheme with two existing schemes namely the ABR scheme and the CAC provision scheme. Performance evaluation results indicate that, for QoS sensitive multimedia services, our scheme strikes the appropriate performance balance between contradictory requirements. Specifically, our scheme maintains well-balanced performance at a small and acceptable penalty in total network efficiency in widely different and diversified traffic load situations while other schemes can not offer such an attractive trade off. REFERENCES [1] Carlos Oliveria, Jaime Bae Kim and Tatsuya Suda, “An Adaptive Bandwidth Reservation Scheme for High – Speed Multimedia Wireless Networks,” IEEE JSAC, vol. 16, no.6, pp. 858-873, August 1998. [2] R. Jayaram, S. K. Sen, N. K. Kakani, and S. K. Das, “Call Admission and Control for Quality – of - Service Provisioning in Next Generation Wireless Networks," Wireless Networks, pp.17-30, February, 2000. [3] Marco Ajmone Marsan, Salvatore Marano, C. Mastroianni and Michela Meo, “Performance analysis of cellular mobile communication networks supporting multimedia services”, Mobile Networks and Applications, vol. 5, no. 3, pp.167-177, 2000. [4] 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. 333–347, 1997. [5] Yongbing Zhang and S. K. Das, “An efficient load-balancing algorithm based on a two - threshold cell selection scheme in mobile cellular networks,” Computer Communications, pp.452-461, March, 2000. [6] Allan Borodin and Ran El-Yaniv, Online Computation and Competitive Analysis, Cambridge University Press, 1998. [7] Sungwook Kim and Pramod K. Varshney, “An Adaptive Bandwidth Reservation Algorithm for QoS Sensitive Multimedia Cellular Network” , IEEE Vehicular Technology Conference, September, 2002. [8] Panagiotis Thomas, D. Teneketzis, J. K. MacKie-Mason, “ A Market based Approach to Optimal Resource Allocation Control in IntegratedServices Connection-Oriented Networks”, University of Michigan, 1999.