Evaluation and Comparaison of Scheduling Algorithms in Wimax Networks

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1 Evaluation and Comparaison of Scheduling Algorithms in Wimax Networks Abstract Kais Mnif * LETI laboratory, University of Sfax, Tunisia Ecole Nationale d Electronique et de Télécommunications (ENET COM) kais.mnif@isecs.rnu.tn In this paper, we propose a new scheduling algorithm for IEEE Broadband wireless Metropolitan Area Networks in TDD mode. Based on some known algorithms such as Modified Deficit Round Robin (MDRR), Proportional Fairness (PF) and Adaptive Proportional Fairness (), we have studied the scheduling problem and proposed scheduling algorithm, called (Enhanced-) for focuses on an efficient mechanism to serve high priority traffic in order to perform and achieve high resource utilization in the Wimax Base Station. We give a detailed simulation study for the proposed scheduling algorithm and its performance has been compared. Based on OPNET modeler Simulation, results show that the proposed algorithm can increase the network throughput, maintain relative fairness, and lower delay of dealing with different requirements from users under congestion conditions. Keywords: Wimax, Scheduling algorithms, QoS. I. INTRODUCTION The Wimax technology is one of the most important broadband wireless technologies. It is based on the IEEE standard and has a very rich set of features. Being an emerging technology, WiMAX can support multimedia applications such as voice over IP voice conference, etc. It is necessary to provide Quality of Service (QoS) guaranteed with different characteristics. Initially, the primary interests of network operators are to improve coverage and spectral efficiency of the Radio Access Network It is a very promising Broadband Wireless Access (BWA) technology. The objective is to have a highly efficient use of radio resources while serving types of services. However, these services can have different constraints such as maximum latency, tolerated jitter and traffic rate. The IEEE BWA system has three possible physical layers (PHY): Single Carrier (SC), Orthogonal Frequency Division Multiplexing (OFDM), and Orthogonal Frequency Division Multiple Access (OFDMA) [1,2]. The OFDMA physical layer is the most efficient and most complex one. In OFDMA each Subscriber Station (SS) can receive some portions of the allocation for the combination of frequency and time so that the channel capacity is efficiently used. Radia Khdhir and Lotfi Kamoun Ecole Nationale d Ingénieurs de Sfax (ENIS) LETI laboratory, University of Sfax, Tunisia Wimax retains only OFDM and OFDMA. For topologies, mesh and Point-to-Multipoint (PMP) modes can be used. Both Duplexing modes are possible: Frequency Division Duplex (FDD) and Time Division Duplex (TDD). Theoretically, a WiMAX base station can provide broadband wireless access in ranges up to 50Kms for mobile stations with a maximum data rate of up to 70 Mbps. An important principle of WiMAX is that it is connection oriented [2]. The IEEE standard Medium Access Control (MAC) defines five types of QoS classes [3]: 1- Unsolicited Grant Service (UGS) UGS is primarily designed for Constant-Bit-Rate (CBR) services such as VoIP, which means that realizing low latency and low jitter is very important. This service provides guaranteed throughput, latency and jitter to the necessary levels as TDM services. UGS has the following set of features: UGS flows are buffered separately from each other and from flows in service classes such as nrtps and BE. UGS service flows are given strictly higher priority versus nrtps and BE service flows, which implies that the system serves nrtps and BE packets only after it has finished transmitting all outstanding UGS packets. 2- Real-Time Polling Service (rtps) The Real-Time Polling Service (rtps) is intended to support real-time service flows that generate variable size data packets on a periodic basis, such as MPEG video. This service offers real-time, periodic, unicast request opportunities, which meet the flow s real-time needs and allow the Subscriber Station (SS) to specify the size of the desired grant. The mandatory service flow parameters that define this service are inclusive of minimum reserved traffic rate, maximum sustained traffic rate, maximum latency and request/transmission policy. This service requires more request overhead than UGS, but supports variable sizes for optimum data transport efficiency. Unlike UGS, the polling overhead exists even when the flows are idle, and for as long as they are active.

2 3- Non-real-time Polling Service (nrtps) This service class is intended to support non-real-time service flows that require variable size data packets, delay tolerant and a minimum data rate, such as FTP. The applications provided by this service are time-insensitive and a minimum amount of bandwidth. This is accomplished by offering unicast polls on a regular basis, which ensures that the service flow receives requests even during network congestion. 4- Extended real-time Polling Service (ertps) This class of service provides real-time applications which generate variable-sized data packets periodically that require guaranteed data rate and delay with silence suppression. This service is only defined in IEEE e During the silent periods, no traffic is sent and no bandwidth is allocated. However, there is a need to have a BS poll during the MS to determine the end of the silent periods. ertps is featured in VoIP with silence suppression 5- Best Effort (BE) The BE service is intended to support data streams that don t require minimum guaranteed rate, and could be handled on best available basis. Unicast polling requests are not guaranteed in this case, requiring contention requests to be used. BE packets may therefore take a long time to transmit during network congestions. A summary of the QoS categories is illustrated in Table 1. TABLE 1. SUMMARY OF QOS CATEGORIES [4] QoS Category Applications QoS specification UGS Unsolicited grant service rtps real-time Polling Service ErtPS Extended real-time Polling Service nrtps Non-real-time polling service BE Best Effort Service VoIP Streaming audio/video VoIP with VAD (Voice Activity Detection) File Transfer Protocol (FTP) Data transfer Web Browsing, etc. Max. sustained rate, Max. latency tolerance, Jitter tolerance. Min. reserved rate, Max. sustained rate, Max. latency tolerance, Traffic priority. Min. reserved rate, Max. sustained rate, Max. latency tolerance, Jitter Tolerance, Traffic priority Min. reserved rate, Max sustained rate, Traffic priority Max sustained rate, The MAC (Media Access Control) layer of IEEE is responsible for allocating resources for SSs (subscriber stations) and active flows is called the scheduling process. Unlike other parts of IEEE , Wimax does not specify the scheduling algorithm to be used. Some works have been published about scheduling algorithms for Wimax but the optimal scheduling algorithm is still in open research area [5], [6] and [8]. In this paper, we did an extensive evaluation of the E- (Enhanced-Adaptive Priority Fairness) scheduling algorithm which is proposed in [9] to provide a better allocation of resources to different SSs (subscriber stations) based on their priority and QoS parameters. The IEEE standard includes the QoS mechanism in the Medium Access Control (MAC) layer (Level 2) architecture. It defines service flows which enables the end-to-end IP based QoS. The MAC layer is also responsible for scheduling of bandwidth for different users based on their requirements as well as the QoS profiles. The standard is designed to support a wide range of applications. These applications may require different levels of QoS [16]. The main objective of this work to compare scheduling algorithms in WiMAX network. In order to specify high network performance, an efficient scheduling algorithm is essential as it manages and controls the provision of an efficient level of QoS support. Although many scheduling algorithms have been proposed in the literature for WiMAX network, the design of the algorithms are challenged by having to support different levels of services, fairness and implementation complexity. The rest of this paper is organized as follows. After an overview of related work concerning scheduling algorithms for Wimax networks in section II, we present, in section III, the four algorithms which will be compared. Section IV presents the setup of simulation environment and the simulation results of the algorithm compared to other algorithms. Finally, concluding remarks and directions for future work are given in section V. II. RELATED WORK In this section, we give an overview of scheduling algorithms presented on the literature. Jain et al. proposed Proportional Fairness (PF) in [10]. The goal of this scheduling algorithm scheme is to enhance the system throughput as well as to provide fairness among the different queues. PF is simple and efficient but it can t guarantee QoS requirements, such as delay and jitter, due to the fact that it was originally designed for saturated queues with non real-time data services. Nie et al. ed survey and study the scheduling problem and propose a two level scheduling () scheme with support for quality of service and fairness guarantees for downlink traffic in a WiMAX network [8] Scheduling algorithms can be classified into two main categories: the first category is based on the channelunaware schedulers while the second category is based on the channel aware schedulers [9]. Channel aware schedulers don t use any instant information of the channel state condition in making the scheduling decision. The design of those schedulers varies based on

3 the ultimate goal of the scheduler such as fair allocation of resources between different SSs or maximizing throughput. However, no single queue algorithm can handle all QoS constrains (jitter, throughput rate, delay, etc.) simultaneously. Indeed, no published researches show how to handle jitter over IEEE and most researches focus on delay or throughput rate. Generally, the QoS classes are served in the following order: UGS, ertps, rtps, nrtps and BE. on the bases of flow priority, where highest priority flow is served first regardless of its class. However, even within the same class, there are many constrains that can t be handled through one scheduler [5]. For example, UGS class is designed to support real time data streams consisting of fixed-size data packets issued at periodic intervals, maximum latency and priority. Most existing scheduling algorithms give precedence to flows based on one or two of those parameters. For instance Round Robin (RR) distributes resources equally over different queues regardless of flow priority and of its maximum latency. There is an extension of the RR scheduler, called the Modified Deficit Round Robin (MDRR) scheduler which focuses on giving priority to the lowest data rate flow regardless of its priority [9]. Other scheduling algorithm, like Adaptive Proportional Fairness () which is designed to extend the PF scheduling algorithm provides a satisfaction for the various QoS requirements [10]. The proposed tries to differentiate the delay performance of each queue based on the Grant per Type-of- Service (GPTS) principle. To sum up, the primary objective of a scheduling algorithm is to be able to share the total system bandwidth fairly. Wimax scheduler aims to achieve the optimal usage of resources, to ensure the QoS, to maximize good put and to minimize power consumption while ensuring feasible algorithm complexity and system scalability. However, it is very difficult for any scheduler to handle all the parameters in one step. In this paper, a new scheduling algorithm is proposed to guarantee the maximum scheduling criteria III. ALGORITHMS COMAPARISON In this section, we give an overview of the four schemes retained for comparaison. III-1. MDRR MDRR scheduling algorithm can be considered as an extension of the DRR scheduling scheme [19].There some different modifications of the DRR scheme and hence the name is MDRR. The algorithm depends upon the DRR scheduling fundaments to a great extent, however, in MDRR the quantum value given to the queues is based on the weight associated with them. The MDRR algorithm adds a PQ (Priority Queeuing) into consideration with DRR. A PQ scheme isolates high priority flows from the rest of the other flows for the reason to guarantee a better quality of service provisioning. There are mostly two types of MDRR schemes [16]: - Alternate Mode: In this mode the high priority queue is serviced in between every other queue. - Strict Priority Mode: Here the high priority queue is served whenever there is backlog after completely transmitting all its packets then the other queues are served. However, as soon as packets are backlogged again in the high priority queue, the scheduler transmits the packet currently being served and moves back to the high priority queue. III-2. Adaptive Proportional Fairness () scheduling for rtps and ertps services, and Proportional Fairness (PF) scheduling for Nrtps and BE services and provides a satisfaction of various QoS requirements. the authors proposed a QoS priority and fairness scheduling scheme for downlink traffic which guarantees the delay requirements of UGS, ertps and rtps service classes. The proposed mechanism is a two-level scheduling scheme that intends to maximize the BE traffic throughput. Firstly, a strict priority between service classes is adapted in the first level as follows UGS > ertps > rtps > nrtps > BE. Secondly, a fixed-size data is granted periodically for UGS service class, an Adaptive Proportional Fairness () scheduling is applied for both rtps and ertps service classes [10] III-3. The scheduling scheme is based on the Grant Per Type-of Service (GPTS) principle, which aims at differentiating the delay performance of each queue. A novel priority function is devised for all the QoS guarantees, including rtps and ertps, for allocating time slots on the queues with the highest priority value. For time t, and user k, the channel quality is Ch Condk (t). This is directly proportional with the signal which the SS receives and determines the maximal transmission rate. Ch Condk (t) is calculated based on modulation, coding and repeat times. For time t, and user k, the historical throughput is Th k (t). The user s priority is calculated by the following formula: P k (t) = Ch Condk (t)/[1+ R i (t)th k (t)/c i ] The user with the highest priority will be scheduled with the highest probability. At the time interval t, the priority function for queue i is defined in below formula. R i (t) is the real-time service minimum rate requirement, C i (t) is the number of connections at the i th queue, channel quality time determines the transmission capacity [9]. III-4. E- The main idea of this scheme is based on that the criteria for user satisfaction such as throughput, fairness, etc. do not really reflect the degree of QoS satisfaction. In fact, allocating the same throughput to all users is not always sufficient to ensure an adequate QoS. It uses the concept

4 of delay outage by analogy with the concept of outage used in system coverage planning. A mobile transmission is in delay outage when its packets experienced a delay greater than a given threshold. The Packet Delay Outage Ratio (PDOR) target is defined as the maximum ratio of packets that may be delivered after this fixed delay threshold. Table 2 shows below presents a comparative analysis of the QoS Scheduling Algorithms. TABLE 2 : COMPARISON BETWEEN ALGORITHMS Scheme Pros. Cons. MDRR E- Enhance the QoS satisfaction Fair and satisfy the QoS requirements Increase the network throughput and lower delay Maximize total throughput Satisfy the main QoS requirements Complex and Unfair Does not consider the channel behavior Complex and need estimation model Does not support the radio channel low average delay Class Supported 3 types of service (UGS, rtps, nrtps) All types of traffic All types of traffic All types of traffic IV. PERFORMANCE SIMULATION AND RESULTS IV-1. Simulation parameter The overall objective of the simulation model is to analyze the behavior and performance of the proposed algorithm in [10] and compared with an existing algorithm such as MDRR, [4], and [11]. The simulations have been performed using Opnet Modeler [15]. The important parameters used to configure the PHY and MAC layers are summarized in table 3. TABLE 3 : PARAMETERS OF SIMULATION MODEL Parameter Model PHY Wimax channel bandwidth Frame duration Symbol duration Number of symbol per PS Initial modulation Value PMP OFDMA 10Mhz 5 ms 12.6µs 4 1/2 QPSK W (bit/symbol) 1 Simulation duration 1000s IV-1. Traffic Model Class 1: VoIP traffic a CBR service, This class is designed to support fixed-sized data packets This service provides guaranteed throughput, latency and jitter. Class 2: Video Conference Traffic. The audio and video components of video conferencing have different bandwidth requirements. The audio component requires between 16 and 64 Kbps and the video component requires between 160 Kbps and 1 Mbps. We implement a typical business-quality video conference which runs at 384 Kbps and can deliver TV-quality video at 25/30 frames per second. The traffic model of video conferencing is shown in table 4 [2]. Class 3: Web Browsing (HTTP) Traffic. An HTTP traffic model is used for the BE class. Each webpage might consist of a number of web objects including embedded images, style sheets, executable java applets, plug-ins, and the page itself. The time between visiting two pages is referred to as the reading time and includes time for the user to read all or part of the page. The session is cut into ON/OFF periods representing webpage downloads and the intermediate reading times, where the webpage downloads are referred to as packet calls. Class 4: FTP Traffic. We implemented an FTP traffic generator with a constant packet size of 1500 bytes. According to packet size distributions, more than 75% of the files are transferred using an MTU of 1500 bytes and almost 25% of the files are transferred using an MTU of 576 bytes. These two packet sizes also include 40 bytes for IP header [2]. TABLE 4: VIDEO STREAMING PARAMETERS Parameter Inter-arrival time between Video frames Video packet size Minimum Reserved Traffic Rate Minimum Sustained Traffic Rate Maximum latency Value 120 ms ~Geometric (mean =200 bytes) 64 kps 500 kps 150ms Tolerated packet loss 3% Average traffic rate 220 kps The simulation environment consists of one BS and the number of the SS, operating in IEEE PMP mode, varies from 5 to 40. There will be one service flow between each SS and the BS. For all SSs, we chose a modulation 1/2 QPSK and each SS will establish one or more sessions to each class of service. Figure 1illustarte the simulation scenario in OPNET for 20 SS.

5 Fig. 1. Simulation scenario in Opnet (20 SSs) IV-2 Analysis results The first set of results consists of varying the arrival rate [ pq/s], for different class of service and showing behavior of the proposed algorithm, figure 2. Simulation results show that we can guarantee to the UGS class with a blocking probability less than 10-4 even with high arrival rate (120 pq/s). Another result consists on respecting the priority of each class and the blocking probability increase linearly as function of arrival rate. Blocking Probability 1,0E-01 1,0E-02 1,0E-03 1,0E-04 UGS rtps nrtps BE 1,0E Arrival Rate (pq/s) Fig.2. Blocking probability vs Arrival rate (pq/s) a- Throughput Throughput is defined as the quantify of data rate (b/s) generated by the application. To calculate throughput the size of each packet was added. The total time was calculated by the difference between the time that the first packet started and the time that the last packet reached the destination. In the second set of results, we will compare the performance of the proposed algorithm Enhanced Adaptive Proportional Fairness (E-) in [10] with the existing algorithms such as MDRR, and. For this comparison, we consider throughput, average delay and packet loss rate. For a given number of SS, we did ten simulations and we compute the average value. Throughput (packett/s) Average delay (ms) Figure 3 illustrates the variation of the Throughput by user expressed in packet/s as a function of the number of SS. According to results from simulation, the E- can perform others algorithm. However, when the number of users is increasing, the E- algorithm causes throughput derogation comparing with the others algorithms. The reason is that the more users in the sector, the larger the probability of users with poor channel condition. To improve fairness among users, the E- has to allocate more resources to users with poor channel condition, causing some degradation of the average sector throughput Number of SS Fig.3. Throughput (pq/s) vs Number of SS b- Average Delay WRR The time taken by the packets to start from the source and reach the destination and traverse back to source is the delay produced by packet. The source which causes the delay can be propagation delay, transmission delay, waiting delay, etc. Figure 4 shows the average delay (end to end delay) for the discussed algorithms. Simulation results prove that the E- gives more interesting results in term of the average of the end to end delay. The E- algorithm ensures that delay sensitive traffic has access to a channel in a timely fashion WRR E- E Number of SS Fig. 4. Average delay (ms) vs Number of SS

6 Packet Loss 1 0,1 0,01 0,001 WRR E Number of SS Fig. 5. Packet loss vs Number of SS c- Packet Loss Packet loss is the sum of all the packets which do not reach the destination over the sum of packets which leaves the destination. The ratio of total data sent to total data lost gives the packet loss. Simulation results given by figure 5 illustrate the evolution of the packet loss when the numbers of SS increase. As it can be concluded, packet loss increases when the number of SS increases. The performance of the E- algorithm exceeds other existing algorithms in term of Packet loss. Indeed, it can guarantee a low packet loss rate even with a high number of SS. CONCLUSION In this paper, an extensive simulation study of four scheduling algorithms (MDRR,, and E-) for resource allocation in IEEE wireless Wimax networks (802.16). The main objective of a reliable algorithm is to enable the BS scheduler to balance between serving high and low priority traffic simultaneously. The simulation results have verified that the E- scheduling algorithm is capable to enhance the performance of Wimax networks. The performance improvement of the E- algorithm is illustrated through the simulation results. The E- algorithm not only meets all the QoS requirements of the service classes but also provides higher throughput, low delay and packet loss rate, while promises the fairness among all the other service class. In a future research work, the algorithm will be extended to the channel interesting challenging task will be focused on the application of the algorithm to the newly established Term Evolution (LTE) networks. REFERENCES [1] IEEE P802.16Rev2/D2, DRAFT Standard for Local and metropolitan area networks, Part 16: Air Interface for Broadband Wireless Access Systems, Dec [2] IEEE Working Group on Broadband Wireless Access. [3] Mokdad L. and Ben Othman J. Performance Analysis of an Admission Control Enhancement in WIMAX networks. Journal of Interconnection Networks Vol. 10 No 4, pp , [4] Nuaymi L., Wimax Technology for Brodband Wireless Access, John Wiley and Sons, [5] Li, B., Qin,Y., Low, C. P. and Gwee, C. L., "A Survey on Mobile WiMAX", IEEE In Communications Magazine, Vol. 45, No. 12, pp , [ 6] Belguith A. and Nuaymi L. Comparison of WiMAX scheduling algorithms and proposals for the rtps QoS class, ENST Bretagne, [7] EL-Shinnawy A., Two-Step Scheduling Algorithm for IEEE Wireless Networks, 12 th Communication Technology (ICCT), [8] Meng X., An Efficient Scheduling for QoS Requirements in WiMAX, Electrical and Computer EngineeringWaterloo, Ontario, Canada, 2007 [9] Nie W., Wang H. and Park H. J. Packet Scheduling with QoS and Fairness for Downlink Traffic in WiMAX Networks, Journal of Information Processing Systems, Vol.7, No.2, June [10 ]Khdhir R., Mnif K. and Kamoun L. ''Performance Evaluation of Scheduling Algorithm for Wimax Networks'' Inter. Conference on Information and Computer Networks ICICN Singapore, Jan , [11] Gueguen C. and Baey S., Weighted Fair Opportunistic Scheduling for Multimedia QoS Support in Multiuser OFDM Wireless Networks, PIMRC2008, IEEE 19th Inernational Symposuim on Personal, Indoor AND Mobile Radio communications, [12] Shreedhar M. and Varghese G., Efficient Fair Queuing using Deficit Round Robin, IEEE/ACM Transaction on networking, Juin [13] Jain R. So-In C., Tamimi A., Scheduling in IEEE e Mobile Wimax Networks: key Issues and survey IEEE journal on Special Area in communication (JSAC), Vol. 27, No.2, February [14] Gueguen C. and Baey S., improving Mobile Satisfaction in Multiuser OFDM wirless Networks, Ecole ete RESCOM June 2008, Saint-Jean-cap-Ferrat. [15] [16] Kaur H. and Singh H. Implementation and Evaluation of Scheduling Algorithms in Point-to-Multipoint Mode in Wimax Networks, InternatIonal Journal of Computer ScienCe and Technology (IJCST) Vol. 2, (3), pp , September 2011.