NATIONAL UNIVERSITY OF SINGAPORE FACULTY OF ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING

Transcription

1 NATIONAL UNIVERSITY OF SINGAPORE FACULTY OF ENGINEERING DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING Performance Study of a QoS Scheduling Algorithm Over Wireless Networks Submitted by Siew HuiPei, Joanna Supervisor Dr. Kong Peng-Yong In partial fulfillment of the requirement for the degree of Bachelor of Electrical and Computer Engineering At the National University of Singapore 2004/2005

2 Contents Abstract Acknowledgments 1. Introduction Objectives Dissertation Overview 4 2. Quality of Service (QoS) Provisioning Quality of Service (QoS) Parameters Quality of Service (QoS) Services Quantitative (Guaranteed) Services Qualitative (Differentiated) Services Best Effort Services Per-flow QoS Services Per-class QoS Services Proposed Service model Difee (Differentiated with Guarantee) Advantages and disadvantages of the various Service Models Summary 18

3 3. Quality of Service (QoS) Wireless Scheduling algorithms Issues in wireless scheduling Specific classes of various wireless scheduling algorithms Channel State System 22 (1) Channel State Dependent Packet Scheduling (CSDPS) 22 (2) CSDPS + Class Based Queuing (CBQ) Ideal Reference System 24 (1) Idealized Wireless Fair Queuing (IWFQ) 27 (2) Channel Condition Independent Fair Queuing (CIF-Q) 29 (3) Wireless Packet Scheduling (WPS) Explicit wireless Compensation Counter 30 (1) Server Based Fairness Approach (SBFA) 30 (2) Improved Channel State Dependent Packet Scheduling 31 (I-CSDPS) Class-based wireless Scheduling System 32 (1) Wireless Multi-class Priority Fair Queuing (MPFQ) Feasible Earliest Due Date (FEDD) Comparison of the various QoS scheduling algorithms Summary Service Curve Dynamic Priority (SCDP) Scheduling Algorithm Motivation and contribution of SCDP 40

4 4.2 Methodology of implementing the Difee Service model in SCDP Fulfilling the Difee Performance Specifications Conditions required Usage of instantaneous actual-arrival and actual-service curve Scheduling algorithm Suggested improvements to SCDP Summary Implementation and Simulation Design of the SCDP Implementation of SCDP Simulation Results and Analysis Summary Summary and Future Research 76 References 82 Appendix 85

5 List of Figures Fig 1.1: Architecture of QoS Control 1 Fig 2.1: Delay and Buffer calculations for a (b, r, p, M) flow 9 Fig 3.1 Typical channel state system scheduler 22 Fig 4.1: Illustration of a scheduler node 41 Fig 4.2: Buffer requirements of scheduler Fig. 5.1: Flow Chart of Enqueue Function 57 Fig. 5.2: Flow Chart of Dequeue Function 58 Fig. 5.3: Code of Enqueue Function in ns-2 59 Fig. 5.4: Code of Enqueue Function in ns-2 60 Fig. 5.5: Code of setting CBR traffic 61 Fig. 5.6: Code of setting Exponential traffic 61 Fig. 5.7: Code of setting Pareto traffic 61 Fig. 5.8: Instantaneous curves of Queue 0 with CBR traffic when rate = 3Mb/s 63 Fig. 5.9: Instantaneous curves of Queue 1 with CBR traffic when rate = 3Mb/s 64 Fig. 5.10: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3Mb/s 64 Fig. 5.11: Instantaneous curves of Queue 0 with CBR traffic when rate = 3.5Mb/s 65 Fig. 5.12: Instantaneous curves of Queue 1 with CBR traffic when rate = 3.5Mb/s 66 Fig. 5.13: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3.5Mb/s 66 Fig. 5.14: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 100ms, Idle Time = 5ms 69 Fig. 5.15: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 200ms, Idle Time = 5ms 69 Fig. 5.16: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 500ms, Idle Time = 5ms 70 Page

6 Fig. 5.17: Proportional Delay violation Ratios of q 0 /q 1 of Pareto Traffic, Burst Time = 100ms, Idle Time = 5ms, Pareto Shape = Fig. 5.18: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3Mb/s 72 Fig. 5.19: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 100ms, Idle Time = 5ms (I-SCDP) 73 Fig. 5.20: Proportional Delay violation Ratios of q 0 /q 1 of Pareto Traffic, Burst Time = 100ms, Idle Time = 5ms, Pareto Shape = 1.5 (I-SCDP) 74

7 List of Tables Page Fig 2.2: Table of comparison between the various Services 16 Fig 3.2: Table of traffic classes and its allocated algorithm in MPFQ 32 Fig 3.3: Comparison of the various wireless scheduling algorithms 36 Fig. 5.1: Measurement of target inaccuracy for SCDP 75 Fig. 5.2: Measurement of target inaccuracy for I-SCDP 75 Fig 6.1: Comparison of SCDP and various wireless scheduling algorithms 78

8 Performance Study of QoS Scheduling Algorithms Over Wireless Networks Abstract With the proliferation of wireless networks, consumers are increasingly aware of the importance and convenience of wireless technology. Wireless packet networks (WLANs) rely on a range of mechanisms to provide for Quality of Service (QoS), the core of which would be scheduling algorithms. In this dissertation, it is recognized that the current QoS Service models have their limitations, and the Difee (Differentiated with Guarantee) Service model is proposed to address the issue of providing both differentiated and a guaranteed performance to different independent flows. After which, the motivation would be to propose a wireless QoS scheduling policy known as the Service Curve Dynamic Priority (SCDP) algorithm, to conform to the Difee performance specification. The focus of this work would be: 1) To provide a literature review on QoS provisioning and the various service models; 2) Proposal of the new Difee Service model; 3) Proposal of the SCDP algorithm to ensure that it adheres to the Difee performance specification - to provide a proportional differentiation on the delay violation ratios between two independent flows; 4) To perform implementation, simulation and a performance study on the proposed algorithm; 5) To identify various problems and issues; 6) Summary of the dissertation and suggestions for future research.

9 Acknowledgments I would like to thank my supervisor, Dr. Kong Peng Yong, who gave many invaluable and insightful comments during my final year project. I feel very fortunate to be able to work with such a supportive and encouraging mentor and supervisor, and I would like to express my heartfelt gratitude towards him and the Institute of Infocomm Research. Special thanks would also go to Long Fei and Wu Bing of ICIS, NTU for their kind orientation to ns-2, Benjamin Coles of Google, Inc for introducing me to Open Source and the wonderful world of Computer Science. Without them, my program would never have been simulated. I would like also like to thank my mom for all her love and patience, and to my daddy dearest who helped me out in so many ways possible. These 4 years of university education was never an easy one. Yet in the midst of all the tears, laughter, joy and sadness, I learnt so many lessons both in and out of the classrooms. And last but not least, I would like to thank God for leading me through this difficult path of my life. Through the darkest, saddest moments of my life, I know that he was there for me, because he was the one, which gave me strength, he was the one who showed me the way, he was the one which loved me the most, and he was the one which never let me go. "I can do everything through him who gives me strength." Philippians 4:13

10 1. Introduction Wireless networks are becoming an integral part of our standard of communication. With the convergence of real-time voice, video and data in multimedia applications, it is essential that service providers provide Quality-of-Service (QoS) to the users in wireless networks. An architecture of QoS control is illustrated in the diagram. packet receiving header processing routing & switching Data plane exception handling Control/ Management plane Medi a I nterf ace normal forwarding classification traffic conditioning systemmanagement & control packet transmitting packet scheduling buffer management queue Fig 1.1: Architecture of QoS Control 1.1 Objectives

11 As the project title dictates, the focus of this thesis would be the performance study of a QoS scheduling algorithm over wireless networks. There are many QoS Provisioning mechanisms which are currently used, namely: 1) Resource Allocation; 2) Admission Control; 3) Congestion Control; 4) Traffic Policing; 5) Scheduling Algorithms or integrated mechanisms like the Cross-Protect Architecture [19] [20]. In this thesis, the performance of a proposed QoS scheduling algorithm would be analyzed. Providing QoS to the consumer in a wireless environment is an ever-increasing problem due to the increasing number of users, congestion, and insufficient bandwidth and so on so forth. The focus would be on wireless technology due to the multitude of advantages of which wireless technology has over wired technology, several are: Less occurrence of failure of equipment. Ease to set up network. Cheaper cost of equipment. Revenue cost is lower in the long term due to less maintenance. Most wireless communication devices are b/g enabled. However, unlike wired networks which assume low error rates, faster data transfers and a fixed number of users, QoS provisioning for wireless networks wireless networks are different due to the unreliability of the channel, the unpredictable resources as well as the

12 dynamically changing conditions. Thus, in the provision of QoS over wireless networks, other factors have to be taken into consideration. Higher probability of error transmission bursts. Dynamically changing number of users. Interference due to instability of signals. Location dependent errors (shadowing, noise, multi-path fading, interference) Varying capacity of the link. Limited bandwidth of channel. Inherently unreliable channels. Bottleneck in downlink of channels (due to limited bandwidth) This dissertation aims to perform a literature review on the current QoS Services and QoS wireless scheduling algorithms. After which, a novel Difee Service model which converges certain qualities of the Guaranteed Services and Differentiated Services is proposed. From this, specific desired qualities of QoS wireless scheduling algorithms are selected and a scheduling algorithm known as the Service Curve Dynamic Priority (SCDP) algorithm is proposed to meet the Difee performance specification. Finally, implementation, simulation and a performance study is done on the proposed algorithm. 1.2 Dissertation Overview This dissertation is organized as follows:

13 Chapter 2 deals with the fundamentals of QoS provisioning, QoS services and the problems and limitations associated with the current service models. We present a new service model called Difee (Differentiated with Guarantee service model), which aims to provide a proportional differentiation on the violation probability of the target performance but not on the target performance itself. Chapter 3 gives an overview of the existing QoS wireless packet scheduling algorithms that are currently implemented in the wireless environment. A comprehensive take on the specific classes of representative algorithms are analyzed and the advantages and disadvantages of each one are evaluated. From this literature survey, specific desired qualities are taken into consideration upon the design of the proposed algorithm. Chapter 4 proposes the Curve Dynamic Priority (SCDP) algorithm based on the performance specifications of the proposed Difee service model to provide a proportional differentiation on the delay violation probability between two independent flows. Suggested improvements for the SCDP algorithm are also proposed. The advantages and disadvantages of the SCDP in relation to the other wireless scheduling algorithms are analyzed. Chapter 5 deals with the implementation, simulation and the performance study of the SCDP algorithm in wireless networks.

14 Chapter 6 gives a summary of the dissertation, as well as directions for future work.

15 2. Quality of Service (QoS) Provisioning In this chapter, the fundamentals of Quality of Service (QoS) provisioning are discussed. The current QoS service models are evaluated and the advantages, disadvantages and limitations associated with the current service models are also analyzed. This chapter focuses on: 1) QoS Parameters, 2) QoS Services, 3) Proposed Service model Difee (Differentiated with Guarantee Service Model), 4) Advantages and disadvantages of the various Service models, 5) Summary. 2.1 Quality of Service (QoS) Parameters QoS provisioning encompasses providing Quality of Service to the end user in terms of several generic parameters, namely:

16 (1) Throughput or bandwidth Throughput is a measure of the date rate (bits per second) generated by the application. Bandwidth is the network resource that is allocated to traffic class depending on its requirements. (2) Delay or latency Delay or latency would be time taken by the packets to transverse from the source to the destination. The main sources of delay can be further categorized into: source-processing delay, propagation delay, network delay and destination processing delay. (3) Delay variation (delay jitter) Delay variation is the variation in the delay introduced by the components along the communication path. (4) Packet loss or corruption rate Packet loss affects the perceived quality of the application. Several causes of packet loss or corruption would be bit errors in an erroneous wireless network, or insufficient buffers due to network congestion when the channel becomes overloaded due to out-of-profile or best effort traffic.

17 2.2 QoS Services QoS Services is further categorized accordingly to the kind of service provided to the applications as well as the entities the network provides services for. When the problem of QoS provisioning is approached, the tradeoffs are analyzed in terms of performance, functionality, security, reliability and complexity for implementation. The categorizations of QoS Services are as follows, namely: 1) Guaranteed Services, 2) Differentiated Services, 3) Best Effort Services, 4) Per-flow QoS Services, 5) Per-class QoS Services. Networks may use a combination of QoS Services in order to support a wider range of applications. Two well-defined frameworks are IntServ (Guaranteed Integrated Services), which provide per-flow and quantitative QoS Services and DiffServ (Differentiated Services), which provide per-class and qualitative QoS Services Quantitative (Guaranteed) Services Quantitative (Guaranteed) Services [1] or hard QoS guarantee the network performance in terms of its generic parameters, namely bandwidth, delay and delay jitter. This service delivers the highest quality of service and they guarantee the network performance in deterministic or statistical terms. The ideal case would be to emulate a dedicated channel with conformant packets, a lossless transmission and an upper and lower bound on the delay requirements whereby the network resources for specific traffic are reserved fully for transmission. The goal is

18 to emulate, over a packet-switched network, the guarantees provided by a dedicated rate circuit. Guaranteed Services are built around the model of rate-based schedulers [2] that aim to approximate a perfect fluid server that guarantees delay by ensuring the user a minimal service rate at any time. Of which it can be represented by the use of Service Curves [3], which provide the traffic envelope that bounds the amount of input traffic. This is simply shown in the diagram below: Number of packets traffic envelope r b M delay buffers requirement R service curve i Cj " + Dj j=1 R time/s Fig 2.1: Delay and Buffer calculations for a (b, r, p, M) flow

19 Here, C j and D j are error terms for the different aspects of the scheduler s behavior, i Cj " + Dj is the latency of the server, r and R are the gradients of the traffic envelope j=1 R and the service curve respectively. Hence, the deterministic and statistical guarantees of Guaranteed Services are achieved using scheduling policies or other mechanisms on an accurately declared traffic profile. The advantages of Guaranteed Services would be its scalability to be used in large packet scheduling networks. It also provides a guaranteed performance level, however, with is, it must be taken in mind that the data path trade-offs are predominantly in terms of scheduler efficiency vs. implementation complexity. The overall scheduler design has to take in mind both the cost of the scheduler as well as system wide resources (link bandwidth and buffer requirements). The disadvantages of Guaranteed Services would be its inability to be used in wireless networks. As seen from chapter 1, when performing packet scheduling in wireless networks, many other factors would have to be taken into consideration due to channel burstiness, location and time dependent errors, as well as its limited bandwidth. In addition to that, Guaranteed Services does not have any mechanisms that allow for differentiation and priority to different classes.

20 2.2.2 Qualitative (Differentiated) Services Qualitative (Differentiated) Services [1] or soft QoS provides relative services depending on the classes of the flows and gives priority to the flow marked with the higher priority. Thus, applications marked with a higher priority in a scheduler will receive the service first as compared to one with a lower priority. Differentiated Services take into account the variation in traffic, the flow differences and the unpredictability of the channel to distribute bandwidth approximately amongst different flows. Differentiated Services does not perform any explicit resource reservation and traffic policing as opposed to Guaranteed Services. It is also subdivided into two different categories, namely: 1) Relative differentiation [4], 2) Proportional differentiation [5]. In relative differentiation, a relative quality ordering can be assured between two flows. This would ensured that the performance of a flow would always be better over that of the other flow. In proportional differentiation, a performance of a flow would always be superior to that of the other flow and this proportional differentiation can be varied and controlled according to circumstances. The advantages of Differentiated Services would be its ease in scalability and its ability to give different priority to different classes. Its ability to differentiate between different

21 traffic classes or categories would also make it a viable choice in implementation in wireless networks. The disadvantages of Differentiated Services would be the absence of a specified performance level. However, this is debatable. Only a small fraction of applications require a strong service guarantee, and a policy which permits adequate provisioning for peak traffic load and protection from lower priority traffic would be sufficient to guarantee a higher overall efficiency of the system without the need to factor in service guarantee requirements Best Effort Services Best Effort Services assumes that all traffic is the same and is treated equally, network services are provided without any performance guarantees. Services that do not require any fixed guaranteed services such as data traffic are best fitted for this service model Per-flow QoS Services Per-flow QoS Services provide service assurance to individual flows, in which the network would provide different services to each application in order to meet its individual needs Per-class QoS Services Per-class QoS Services provide service assurance to individual classes such that applications in the same class will experience the same QoS. The categorizations of these

22 applications are based on criteria such as application types, protocols types and QoS requirements. 2.3 Proposed Service Model Difee (Differentiated with Guarantee) Services In view of the advantages and the disadvantages of the Guaranteed and Differentiated Services, it can be seen that although both service models are scalable and implemented in a large packet-scheduling network, however, both lack specific features essential in QoS provisioning. Namely, Guaranteed Services do not have mechanisms that deal with variability in traffic classes and system wide resources, and Differentiated Services do not deal with guaranteeing a certain performance level. With this, the concept of Difee (Differentiated with Guarantee) is proposed. Difee aims to combine both Guaranteed and Differentiated Services to provide form a single performance specification. The realization of which would be to provide a proportional differentiation on the packet delay violation ratios amongst different flows whereby the flows have different maximum packet delay requirements. With this, the thesis aims to highlight several definitions pertaining to the performance specifications of the various service models.

23 Definition 2.1 [Performance Specification of the Guaranteed Services] Guaranteed Services provide network performance for independent flows in either deterministic or statistical terms. Thus: For deterministic lower bound guarantees, P[qi " qi*] = 0 For deterministic upper bound guarantees, P[qi > qi*] = 0 For statistical lower bound guarantees, P[qi " qi*] " #i For statistical upper bound guarantees, P[qi > qi*] " #i Here, qi * would be the targeted performance level, qi would be the actual performance level and "i would be the bounding parameter for flow i. As it can be seen from statistical guarantees, a delay violation may be permitted for Guaranteed Services depending on the bounding parameter specified. Definition 2.2 [Performance Specification of the Differentiated Services] Differentiated Services provide relative services to different flows depending on the classes and flow priority. Differentiated Services are further subdivided into relative differentiation and proportional differentiation. The performance specification of independent flows would be defined as such:

24 For relative differentiation, qi* " qi # 1* For proportional differentiation, qi* = "i.qi # 1* Here, qi * would be the targeted performance level, qi would be the actual performance level, and "i would be the parameter defining the proportional relationship between flow i and flow i-1. Definition 2.3 [Performance Specification of Difee (Differentiated with Guaranteed)] Difee (Differentiated with Guarantee) aims to provide a performance specification that converges both Guaranteed and Differentiated Services. The performance specification of independent flows would be defined as such for both relative and proportional differentiation: For relative differentiation: For lower bound guarantees, P[qi " qi*] " P[qi # 1 " qi # 1*] For upper bound guarantees, P[qi > qi*] " P[qi # 1 > qi # 1*] For proportional differentiation: For lower bound guarantees, P[qi " qi*] = #i.p[qi $ 1 " qi $ 1*] For upper bound guarantees, P[qi > qi*] = "i.p[qi # 1 > qi # 1*]

25 Here, qi * would be the targeted performance level, qi would be the actual performance level, and "i would be the parameter defining the proportional relationship between flow i and flow i-1. Thus, the relative and proportional differentiation between two independent flows is not correlated by qi, but on the violation probability of qi * as defined in Definition 1. From the defined performance specification, it can be seen the novelty of Difee would be not be on service guarantees and differentiation of the independent flows, but on the differentiation on the violation probabilities of the target performance and not on the targeted performance itself. 2.4 Advantages and disadvantages of the various Service models As it can be seen from the discussion in chapter 2, the various Service models have their advantages and disadvantages. In this thesis, a novel Service model known as Difee (Differentiated with Guarantee) has been proposed to converge both Guaranteed Services and Differentiated Services. A brief summary of the pros and cons of each of the Service models is summarized in the table below:

26 Guaranteed Services Differentiated Services Best Effort Services Difee Scalability Yes Yes Yes Yes Guaranteed Performance level Yes No No No Suitability for wireless networks (Unpredictable No (Bursty traffic) Yes No Yes resources) Differentiation between classes No Yes No Yes Differentiation on the violation probability of targeted performance No No No Yes Fig 2.2: Table of comparison between the various Services. As it can be seen from the above table, there are limitations to the three Service models currently implemented. With emphasis on QoS provisioning in wireless networks, Guaranteed Services are inappropriate when there is unpredictable or bursty traffic due to the lack of being able to provide a service guarantee in terms of the QoS parameters as defined in Section 2.1. Differentiated Services can be ported into a wireless network and provides differentiation and priorities between various classes; however, it does not guarantee a performance level as that of Guaranteed Services.

27 Best Effort Services do not even provide any performance guarantee or differentiation between traffic classes and everything is treated the same. However, the proposed Service model, Difee (Differentiated with Guarantee) Services does not only provide a differentiation on the different traffic classes, but also a differentiation on the violation probability of targeted performance. Furthermore, Difee can be used in wireless networks whereby the traffic may violate the targeted performance specified due to the unpredictability of traffic resources as well as erroneous channels. 2.5 Summary In this chapter, the foundations of QoS provisioning are discussed. The QoS parameters that are required to support QoS provisioning in multimedia applications are briefly mentioned, and the different kinds of QoS services are discussed upon and evaluated. Upon examining the various service models and their pros and cons, a new service model, Difee (Differentiated with Guarantee) is proposed to satisfy the performance specification of both Guaranteed and Differentiated Services. Difee aims to provide a differentiation on the violation probability of a targeted performance but not on the targeted performance itself.

28 3. Quality of Service (QoS) Scheduling Algorithms in Wireless Networks In this chapter, a comprehensive and thorough literature review of the current developments in wireless scheduling algorithms is performed. In quality of service (QoS), the QoS differentiation and guarantees are pivotal in the development of wireless networks. With this, this chapter focuses on: 1) Issues in wireless scheduling, 2) Specific classes of various wireless scheduling algorithms and their directions, 3) Advantages and disadvantages of the various QoS scheduling algorithms are compared and analyzed, 4) Summary of scheduling algorithms. Most wireless scheduling algorithms are also based on the assumption that scheduling is done downstream and full control is enabled over the wireless medium.

29 3.1 Issues in wireless scheduling Prior to analyzing the various scheduling algorithms, the issues of wireless scheduling would be discussed. Such that it can be gauged what is of importance in the implementation and development of a wireless QoS scheduling algorithm. The five specific areas of which would be discussed upon would be: 1) Quality of service (QoS), 2) Simplicity in implementation, 3) Wireless link variability, 4) Bandwidth utilization Quality of service (QoS) QoS differentiation and guarantees must be supported for heterogeneous classes of traffic with different QoS requirements in wireless networks. To achieve this goal, the corresponding mechanism for QoS support has to be integrated into the scheduling algorithm, and is dictated by the service models as defined in Chapter 2. Deterministic or statistical guarantees must be determined on the links whereby the physical channel degradation does not exceed the specified threshold Simplicity/Complexity in implementation Scheduling algorithms should be low in complexity and implementation. Due to the high velocity and stringent timing requirements of the packets arriving at the scheduler, a scheduler with low implementation effort would be ideal for the execution of selection of packets to be transmitted to the outgoing link.

30 3.1.3 Wireless link variability In a wireless channel, the capacity of the wireless link has very high variability, and suffers from interference, fading and other complications. As such, due to the burstiness of traffic reaching the scheduler, scheduling algorithms would have to be equipped with a dynamically changing mechanism, which permits time-dependent changes Bandwidth/Channel utilization Due to the limitation of bandwidth in wireless networks, scheduling algorithms aim to minimize idle transmission on error links, maximize total service ensured as well as effectively distribute the bandwidth to all incoming links to the scheduler. 3.2 Specific classes of various wireless scheduling algorithms Wireless scheduling algorithms can be categorized into several classes and be represented by several scheduling algorithms. In spite of the whole assortment of scheduling algorithms under development and research, we can focus on several representative algorithms, as most of the others would be variants of these specific classes. In this thesis, a literature survey would be done on: 1) Channel state system, 2) Ideal reference system, 3) Wireless compensation system, 4) Class based system, 5) Earliest due date system.

31 3.2.1 Channel State System The channel state system addresses the problem of location-dependent errors and burstiness due to wireless traffic. Scheduling algorithms which follow this Channel state system, attempt to rectify these problems of erroneous channels via wireless scheduling. A typical scheduler which follows the Channel State System is illustrated here: Flow 1 Flow 2 R 1 R 2 Queue 1 Scheduler Destination Flow i R i Queue i C j Link States Monitor Fig 3.1 Typical channel state system scheduler (1) Channel State Dependent Packet Scheduling (CSDPS) Channel State Dependent Packet Scheduling (CSDPS) [6] addresses the problem of located dependent errors through the use of a Link States Monitor (LSM). Each queue are independent of each other, and within the queue, packets are serviced in a First In First

32 Out (FIFO) order. Across the queue, the scheduling policy to be determined would be according the service requirements of the particular queue. The LSM monitors the link state for all mobile hosts in the region, when a link experiences bursty errors, the scheduling algorithm defers transmission of packets on the link and the LSM marks the link to be in a bad state, and the scheduler does not service the marked queue. The advantages of CSDPS would be that it improves performance by taking into the location dependent and time dependent channel states and using LSM to feedback into the scheduler. It allows for a high data throughput, good channel utilization, it reduces the delay of packet by alleviating the HOL (Head of Line) blocking problem caused by FIFO due to the absence of links in bad states. Moreover, it requires a medium implementation effort. The disadvantages of CSDPS is that there are no guaranteed bandwidth, no guarantees on packets delay and that a user may receive less service opportunities as a link may be accidentally marked by the LSM and perceived to be in a bad state. (2) Channel State Dependent Packet Scheduling + Class Based Queuing (CSDPS + CBQ) A Class Based Queuing (CBQ) [7] is an addition to CSDPS and it solves the problem of unfair bandwidth sharing. Different traffic classes are assigned to different classes and

33 are given a specific percentage of bandwidth over a predefined time interval. Thus, it would solve the problem of fairness in CSDPS. The advantages of CSDPS + CBQ is that the algorithm would enable fair sharing in a wireless channel whilst attempting to maintain a high throughput. The disadvantages to CSDPS + CBQ is that it does not have an explicit mechanism for compensating mobile users which have lost their service due to accidental marking of the links by the LSM. CSDPS + CBQ also does not solve the other QoS issues such as delay bound and packet loss rate. Implementation of the algorithm is also high Ideal Reference System Wireless scheduling algorithms, which schedule packets with reference to an error free system, are known as ideal reference system algorithms. A flow is said to be leading, lagging or in sync at any time instant by comparing its queue size with that of the one in an error-free system. The scheduler will thus perform scheduling and maintain fairness according to this knowledge. (1) Idealized Wireless Fair Queuing (IWFQ) Idealized Wireless Fair Queuing (IWFQ) [8] [9] is an adoption of Weighted Fair Queuing (WFQ) [10], which approximates Generalized Processor Sharing (GPS) on a packet level. An IWFQ model is defined with reference to an error-free WFQ service system. This

34 scheme is idealized due to three factors: 1) Packets are never dropped or lost, 2) Channel state is always known, 3) Presence of a perfect MAC protocol. When no link suffers from bursty errors, IWFQ performs exactly like its wireline WFQ counterpart. When a packet n of flow i arrives at the scheduler, it is tagged with a service start time S i, n and a service finish time f i, n : Si, n = max{v(a(t)), fi, n " 1 fi, n = Si, n + Li, n ri Here, L i, n is the packet length, v(a(t)) is the system virtual time as defined in WFQ and r i is the service rate allocated to flow i. Packets are stored in a non-decreasing order of finish times in each queue, and the scheduler would serve the packet with the smallest finish time. However, when there is an occurrence of a bad link in a wireless scenario, and the packet to be served cannot be served, the next packet with the smallest finish time would be served instead. This would perpetuate until the scheduler find a packet with a good link state.

35 The essential difference is that in a wireless environment, there is implicit wireless compensation, whereby lagging packets may be discarded if the flow lags by too much without losing precedence in channel access, and users which experience error bursts will get precedence upon having good transmission conditions. To compensate for the short error-bursts, IWFQ performs local adjustments in the channel allocation by the use of tags. A flow, which is initially denied service due to burst errors in the packets, will eventually receive service, as its service tag does not change. And hence backlogged lagging flows will receive precedence over leading flows upon attainment of a good channel. The advantages of IWFQ lie in the fact that it provides fairness in the system and QoS guarantees. The disadvantages of IWFQ is its high implementation complexity due to the need to simulate an error free system and keep track of both services to the error-free and real system. And as IWFQ requires packet arrival times to compute virtual start times, the base station requires information of uplink packet arrival times, which is unknown. Furthermore, as absolute priority is given to packets with the smallest finish times, when a flow is compensated for its previous lagged service all other error-free flows would not be served at all, the delay and throughput guarantees are also closely coupled, which may lead to one affecting the other.

36 (2) Channel-Condition independent Packet Fair Queuing (CIF-Q) Channel-Condition independent Packet Fair Queuing (CIF-Q) [11] utilizes an error-free fair queuing reference system as it attempts to approximate the real service to the ideal error free system. With the location dependent errors in mind, CIF-Q tries to use the ideal system as the reference in order to perform wireless scheduling. Due to the discrepancies between users in a wireless networks, CIF-Q aims to provide delay, bandwidth and short term fairness guarantees for error free sessions and long term fairness guarantees among error sessions. In addition to that, sessions which have received additional services are permitted a graceful degradation in QoS. To simulate this model, Start-Time Fair Queuing (SFQ) [12] algorithm is used to implement this model. This is due to its simplicity in performing scheduling on the start time than the finish times. For low throughput flows, SFQ provides a small average and maximum delay. Upon arrival, each packet is assigned a start and finish tag based on the arrival time of the packet and the finish tag of the previous packet, and the packets are scheduled in increasing order of their start tags. In CIF-Q, packets are scheduled in accordance to their start tags. If in any situation whereby there are location-based errors, service is transferred to another flow, and the

37 virtual time of that flow in the reference system is updated. Thus the virtual time of the flow is updated to record the normalized work such that: vi = vi + li ri Whereby l i k is the length of the k th packet of session i and r i is the rate of session i. As and when the selected flow is unable to be transmitted, the bandwidth is distributed among the other flows proportional to their flow rates. Furthermore, the CIF-Q is delay bounded as when a lagging session wants to leave, the lag variable is increased proportional to their rate, and a leading session is not allowed to leave until it has given up its lead. The advantages of CIF-Q would be its fairness guarantees both short term and long term. Additionally, CIF-Q advantageous over IWFQ as it improves scheduling fairness by associating compensation rate and penalty rate with a flow s allocated service rate and guaranteeing flows with error-free links with a minimal rate. In error-free links, CIF-Q also guarantees packet delay bounds as well flow throughput. The disadvantages of CIF-Q are similar to that of IWFQ. First and foremost, CIF-Q requires packet arrival times to compute virtual start times; the base station requires

38 information of uplink packet arrival times, which is unknown. The need to simulate a reference system also increases the complexity. (3) Wireless Packet Scheduling (WPS) Wireless Packet Scheduling (WPS) [13] is the approximation of IWFQ, which uses Weighted Round Robin (WRR) as its error-free reference algorithm. WRR is used in the implementation due to its simplicity in implementation and its O (1) time to process a packet in wireless networks. Moreover, in comparison with other fair queuing algorithms, the performance is very similar. In WPS, to account for the burstiness of wireless channels, WRR is modified with two different types of wireless compensation, 1) intra-frame swapping, 2) credits/debits. For intra-frame swapping, the algorithm will attempt to swap the frame of a flow with various error bursts with one, which is experiencing a good channel. For credits/debits, for a backlogged flow in a queue that is unable to intra-frame swap and unable to transmit, it is assigned a credit if there are one or other flows, which are able to use the slot. In turn, their credit is decremented. The advantages of WPS are that it provides short term and long-term fairness guarantees as well as QoS guarantees. Moreover, as it uses WRR as its referencing system, the algorithm is simpler to implement.

39 The disadvantages of WPQ is similar to IWFQ and CIF-Q, as WFQ requires packet arrival times to compute virtual start times; the base station requires information of uplink packet arrival times, which is unknown Explicit wireless Compensation Counter (1) Server Based Fairness Approach (SBFA) Server-based fairness approach (SBFA) [14] introduces the concept of a compensation server (LTFS), which allows for long-term fairness. With this, a portion of the wireless bandwidth is allocated to the LTFS and it distributes bandwidth with other sessions and provides capacity to them in order to maintain long-term fairness guarantees. The LTFS will thus compensate flows whose packet transmissions are deferred due to link errors. As sessions associated with specific LTFS share its bandwidth, in implementation of the SBFA, sessions with similar QoS requirements should be assigned to the same LTFS to maintain fairness of the system. The advantage of SBFA is fact that it is simple to implement and provides throughput guarantees. The disadvantages of SBFA are that the LTFS requires pre-allocated network resources, and that it cannot be dynamically changed. Furthermore, the packet size must always be

40 constant in order for the system to calculate virtual time. SBFA is also based on the assumption that all flows in a good state should always be served at its designated service rate and not a fraction of its promised rate, and no restrictions are imposed on excessive service. Thus, a flow with a good link may receive more service then its supposed share. (2) Improved Channel State Dependent Packet Scheduling (I-CSDPS) Improved Channel State Dependent Packet Scheduling (I-CSDPS) [15] is a scheduling algorithm which converges deficit round robin (DRR) with a wireless compensation counter. To allow flows to receive compensation for its lost service opportunities due to bad links, I-SCDPS adds a compensation counter (CC) to each flow. The CC keeps track of the amount of lost service for each flow. If the scheduler defers transmission of a packet because of link errors, the corresponding deficit counter (DC) is decreased by the quantum size (QS) of the flow and the CC is increased by the QS. The advantages of I-SCDPS would be its flexibility and feedback mechanism, of which its compensation rate is dynamically adjusting accordingly to the system load, and its ability to handle packets of variable packet lengths. The disadvantages of I-SCDPS would be that it does not impose any restrictions on flows receiving excessive service, and there would be a tradeoff between the packet delay

41 bound and the degree of compensation a flow would be permitted to receive for its lost services due to link errors Class Based Wireless Scheduling System (1) Wireless Multi-class Priority Fair Queuing (MPFQ) In Wireless Multi-class Priority Fair Queuing (MPFQ) [16], a class-based approach is merged with a flow based scheduling. It introduces the concept of mapping ATM traffic classes in the wireless domain to that of the wireless channel, which specifies each traffic class, a specific scheduler. Priority is given to the packets depending on the bit rate and if the flow is streamed or elastic. The table below lists the specific traffic classes, its priority as well as its allocated scheduling algorithm. Traffic Classes Priority Scheduling Algorithm CBR (real time, Wireless Packet 1 constant bit rate) Scheduling (WPS) VBR (real time, Wireless Packet 2 variable bit rate) Scheduling (WPS) GFR, nrtvbr, ABR-MCR Weighted Round Robin 3 (non real time, (WRR) guaranteed bit rate) ABR-rest (best effort) 4 Recirculating FIFO UBR (pure best effort) 5 FIFO Fig 3.2: Table of traffic classes and its allocated algorithm in MPFQ

42 In error-free wireless transmission, first priority traffic as specified in the table would have the same delay bounds as the WPS scheduling algorithm. After which, subsequent priority traffic would be scheduled in the algorithm. In order to determine the lead or lag of a flow, MFPQ tracks two time variables: 1) Current virtual time, 2) Shadow virtual time. When there is an erroneous channel and the link is marked as bad, the virtual time is forwarded to when the packet should be transmitted, and the shadow virtual time is updated to when the packet should have been sent. Thus, the shadow virtual time keeps track of the time in the error-free system and the lead or lag of the flow is the difference of packet tag to its shadow virtual time. For packet i in the flow: Lead(HOLi) = (( fi, n " Lp R ) " ts)#ni Lag(HOLi) = (ts " ( fi, n " Lp R ))#Ni Here, t s is the shadow virtual time, fi, n is the finish time of the packet i, L p is the length of the packet, R is the rate at which the packet is being sent, and "Ni is the proportional parameter when compared to other flows.

43 Wireless compensation in MPFQ is also dependent on its classes. For classes with higher priority, a lagging flow of a lower priority class is forced to give up their fraction of their bandwidth to that of a higher class priority. No compensation is done on the lowest class traffic. The advantage of MPFQ is that its QoS guarantees are according to its specified wireless scheduling algorithms. The disadvantages of MPFQ is that implementation is both an expensive and extensive effort, as it requires effort to compute and classify each and every flow into its various classes as well as provide for wireless compensation Feasible Earliest Due Date (FEDD) The feasible earliest due date (FEDD) [17] policy is a scheduling policy which is an adaptation of earliest due date (EDD). The link is marked to be in either a good or bad state, and the packet in a good state link with the earliest deadline to expire first is scheduled first. The advantage of FEDD is that it minimizes packet losses caused by the deadline expiry and it is has a medium implementation effort.

44 The disadvantage of FEDD is that it does not discuss fairness issues and other QoS guarantees. 3.3 Comparison of the various QoS scheduling algorithms As we have discussed in the previous section, the various QoS scheduling algorithms have both its advantages and disadvantages. In this section, we would like to do a comparison between the various QoS scheduling algorithms presented. In this table, specific aspects of the wireless scheduling algorithm are analyzed and compared with each other, namely: 1) Wireline reference algorithm wireless scheduling algorithms which utilize the ideal reference system would have a wireline reference algorithm, however, this would mean more implementation effort in simulating both the reference and the real time system, 2) Delay bound delay bounds are defined only for flows with error-free wireless links, as bounds for erroneous channels cannot be determined, 3) Bandwidth guarantee long term bandwidth guarantee means that the throughput of a system can be maintained above a certain value if the flow has enough service demand and the link errors are infrequent, 4) Wireless Link Variability the algorithm has specific measures to ensure that it can be used in a wireless domain, 5) Short-term fairness, 6) Allocated capacity for compensation a portion of the bandwidth is distributed such that the system can maintain long term fairness, 7) Differentiation between classes the ability of the scheduling algorithm to give different priorities to different traffic classes.

45 These aspects are further compared in the table below: Wireline ref. Algorithm Bandwidth Delay Bound Guarantee Wireless Link Variability Short term Fairness Allocated Differentiation capacity for between classes compensation CSDPS N/A No No Yes No No No CSDPS + N/A No Yes Yes No No No CBQ IWFQ GPS ref. Yes Yes Yes No No No CIF Q SFQ ref. Yes Yes Yes Yes No No WPS WRR ref. Yes Yes Yes No No No SBFA N/A No Yes Yes No Yes No I-CSDPS N/A Yes Yes Yes No No No MPFQ N/A Class dependent No Yes No No Yes FEDD N/A No Yes Yes No No No Fig 3.3: Comparison of the various wireless scheduling algorithms As it can be seen from the table, there is no scheduling algorithm, which achieves all the desired qualities of wireless scheduling. There is always a tradeoff in doing so. From this analysis and review of the current wireless scheduling algorithms, we can set out a set of objectives, which it is felt that the wireless scheduling algorithm should meet.

46 Specifically, 1) Provide long-term fairness and throughput guarantees, 2) Achieve high wireless channel utilization, 3) Minimize packet loss, 4) Provide a delay bound for flows with bad erroneous channels, 5) Support multi class differentiation of traffic classes for QoS differentiation, 6) Simple to medium implementation and algorithm complexity. 3.4 Summary In this chapter, we presented a inclusive and comprehensive literature survey on the issues pertaining to wireless scheduling, as well as the representative scheduling algorithms currently available. The wireless scheduling algorithms were than analyzed and a comparison done between each of them in specific areas, which are felt to be important and pertaining to QoS provisioning.

47 4. Service Curve Dynamic Priority (SCDP) Scheduling Algorithm In this chapter, a novel wireless scheduling algorithm known as the Service Curve Dynamic Priority (SCDP) algorithm is proposed based on the Difee (Differentiated with Guarantee) Service Model presented in chapter 2. The rest of the chapter is organized as follows: 1) Motivation and contribution of SCDP; 2) Methodology of implementing the Difee (Differentiated with Guarantee) Service model in SCDP; 3) Suggested improvements to SCDP; 4) Summary. 4.1 Motivation and contribution of SCDP The Service Curve Dynamic Priority (SCDP) algorithm was proposed based on the need to satisfy the Difee performance specification as defined in Definition 2.3 in Chapter 2,

48 which would be a proportional differentiation [5] [21] on the violation probabilities of the target performance and not on the targeted performance itself. As such, the SCDP is proposed to provide a proportional differentiation on the delay violation ratios between two independent flows. In packet scheduling, we have critically analyzed several of the available scheduling algorithms implemented in the wireless domain in Chapter 3. It is often a tradeoff in attempting to achieve the desired qualities of wireless scheduling. Moreover, the requirement for low complexity and scalability limits the number of practical scheduling algorithms available for implementation, with weighted round robin [22] [23] and simple priority scheduling being recognized as two of the most scalable algorithms available as of the present moment. As specified in chapter 3, there were several objectives put forth in the selection of a desired wireless scheduling algorithm. It is virtually impossible to propose a scheduling algorithm that befits every single objective, however, with this in mind, specific qualities which it is felt to be more essential was put into thought upon the formulation of SCDP. Namely: 1) Achieve high wireless channel utilization; 2) Minimize packet loss; 3) Support multi class differentiation of traffic classes for QoS differentiation; 4) Simple implementation and algorithm complexity. In addition, SCPQ would have wireless link variability, a dynamic control system which adjusts to maintain a proportional differentiation between flows; it would have a low

49 computational ability, disregard the need for wireless compensation and conform to the Difee performance specification. 4.2 Methodology of implementing the Difee Service model in SCDP In this section, the methodology of implementation of the Difee Service model in SCDP is analyzed. The objectives would be carried out as such: 1) Fulfilling the Difee performance specification in a scheduling algorithm; 2) Conditions required for the implementation of Difee; 3) Usage of instantaneous actual-arrival and actual-service curves; 4) Steering parameters in the scheduling algorithms Fulfilling the Difee performance specification From chapter 2, Definition 2.3, the Difee performance specification for two independent flows are specified as: For proportional differentiation: For lower bound guarantees, P[qi " qi*] = #i.p[qi $ 1 " qi $ 1*] For upper bound guarantees, P[qi > qi*] = "i.p[qi # 1 > qi # 1*] Of which Difee would be to provide a differentiation on the violation probability of a target performance but not on the target performance itself. Thus SCDP would have to have a violation probability in order for the scheduler to fulfill the Difee performance

50 specification. Using this violation probability, the scheduler will then attempt to perform proportional differentiation on two independent flows going into the scheduler Conditions required The aim would be to find a violation probability of which proportional differentiation can be performed in order for the Difee performance specification to be fulfilled. A network node is illustrated below. Flow 1 Flow 2 R 1 R 2 Queue 1 Scheduler Destination Flow i R i Queue i C j Fig 4.1: Illustration of a scheduler node We consider a scheduler node with i flows entering the scheduler. Buffer management is implemented at the queues to buffer a fixed number of packets and packets exceeding the queue length are dropped. Packets leave the queues when they are serviced. It can be seen from the network node that there can be two scenarios arising from the rate at which the packets are entering into the scheduler. Specifically:

51 1) If the summation of all the rates of flows entering the scheduler were lower than the specified capacity of the outgoing link, the system would be stable and in a steady state. This is be dictated in the equation below. R1+ R2 + R3 +...Ri " Cj i"# # R " C j (1) Whereby R 1, R 2. R i is the rate of the incoming links and C j is the capacity of the channel. 2) If the summation of all the rates of flows entering the scheduler were higher than the specified capacity of the outgoing link, the system would be unstable. This is illustrated in the equation below. R1+ R2 + R3 +...Ri > Cj i"# " R > C j (2) However, in a wireless communication channel, traffic patterns are often difficult to predict, and thus the occurrence of (2) is high. Thus there would be a high possibility of violating the guaranteed performance specification, i.e P[qi > qi*] = 0.

52 With this, the SCDP is formally introduced. Wireless scheduling algorithms use an assortment of mechanisms to provide for wireless link variability. However, most require an extensive and complex implementation such as having to simulate another error-free system in the Ideal reference system, or having wireless compensation in the Compensation counter system. With this, the usage of instantaneous actual-arrival and actual service curves are explored. The assumption of SCDP would be downlink traffic, with independent flows Instantaneous actual-arrival and actual-service curve Number of Packets a i s(t) F i s(t) s i s(t) q i * Time Fig 4.1: Illustration of the instantaneous actual-arrival and actual-service curve Here, a i s(t) is the instantaneous arrival curve, F i s(t) is the idealized service curve, s i s(t) is the actual service curve and q i * is the delay guarantee of flow i.

53 Definition 4.1 [Instantaneous Arrival Curve] The instantaneous arrival curve for flow i, a i s(t) is defined as a non-decreasing, non-negative function such that a i k(t) " a i k+1 (t) for any packet k entering the scheduler and being enqueued into the queue at time t. Definition 4.2 [Idealized Service Curve] The idealized service curve for flow i, F i s(t) is defined as a non-decreasing, non-negative function and a time shifted curve of the instantaneous arrival curve such that F i s(t) = a s (t-q i *). It is stated to be the minimum actual service required within the time duration t, to meet the guaranteed performance specification such that no delay violation occurs. F i s(t) is iteratively defined by: F i s(t) = max[a s i, min(s s i-1, F s i-1 )] + li Rs (3) Here, F i s(t) is the target departure time for the ith packet of the flow, a s i is the arrival time of the packet at the scheduler, l i is the length of packet and R s is the rate of the flow. The delay guarantee of the flow can be defined by: q i * = max[a s i, min(s s i-1, F s i-1 )] (4) Definition 4.3 [Actual Service Curve] The actual service curve, s i s(t) is defined as the non-decreasing, non-negative curve of which a packet s of flow i is serviced and departed from the scheduler at time t.

54 For all i 0: s i s = F i s + E s (5) Whereby F 0 s = 0 and s 0 s = 0 Here, E s would be the delay violation of which the flow has failed to fall within the service guarantees; manipulation of (5) would result in the delay violation, d s to be: d s = s i s - F i s (6) However, the traffic arrival of the flows upon reaching the scheduler in a wireless domain is often unpredictable, and the condition of (2) could be very likely, making delay violation very probable. With this, we need to take into consideration of the possibility of the service curve of the flow not falling into targeted departure time or the idealized service curve Scheduling algorithm From Definition 2.3, P[qi > qi*] can be written as the ratio of {qi > qi* to the total number of qi recorded. With this and knowledge of the instantaneous actual-arrival and actualservice curve:

55 ss " Fs P[qi > qi*] = lim t"># qi * = lim di t"># qi * (7) Definition 4.3 [Delay violation ratio] The delay violation of flow i is defined as: "i = di qi * (8) In comparing (7) and (8), for short time scales, they can be approximated. Hence, meeting the Difee performance specification becomes a goal of achieving a proportional differentiation on the instantaneous delay violation ratios between two flows; each would have its own maximum delay requirement qi *. Thus, in selection of the packets to be serviced, packets with a lower delay violation ratio would be served with a greater priority as opposed to flows with a higher delay violation ratio. Packets which fall under condition (1) would be served by FIFO. In an ideal situation, the proportional differentiation between the delay violation ratios of flow i and flow j would be defined as: "i = "j #i #j i, j"# (9)

56 Here, "i and "j would be the delay violation ratios, "i and "j would be the differentiation parameter of users i and j respectively. A larger "i would mean a larger delay violation ratio, "i, of which the flow would take a lower precedence to be served. Thus, a perfect proportional differentiation occurs when "i $ "j #i #j = 0 for "i, j. The target inaccuracy bound would be defined as such: " # $i & $j %i %j (10) And the maximum inaccuracy bound would be defined as such: " = #i % #j $i $j Hence, the steering parameter to determine which flow is to be serviced would be determined proportionally by "i = "j #i #j. With this, the overall goal of SCDP would be to provide a proportional differentiation on the delay violation ratios within the target in accuracy bound.

57 Definition 4.4 [Buffer requirements] In order for delay violation to occur, the arrival rate to the link must exceed the capacity as seen in (2). However, with this, packets would be dropped if no buffer is provided. Buffering is required when congestion arises. From literature, congestion can be subdivided into two conditions, specifically: 1) short interval of transient overloading; and 2) prolong periods of overloading due to out-of-profile traffic. Short intervals of transient overloading are commonplace in an erratic wireless environment with bursty out-of-profile traffic. However, it is noted that this is normally temporary and would dissipate when " Ri fall below the link capacity, and buffer management would be the simplest solution to the problem. For the latter scenario, it is noted that basic buffering would not be sufficient to cater for the increasing amount of backlogged packets in several congestion. With this, more complicated congestion control techniques have to be implemented either by discarding excess packets which fall out of the buffer, or by reducing the source generation rate of packets. The buffer requirements can be defined as such:

58 Number of Packets A s (t) S s (t) B min t Fig 4.2: Buffer requirements of scheduler For flow i, the arrival curve A s (t) and service curve S s (t) for number of packets, s = 1, 2...n converging at node j, the minimum buffer size to protect for the lost of traffic is would be the maximum vertical separation between the arrival and service curves. B is min therefore the maximum buffer threshold required for adequate protection against packet drops. n B min = {#(As(t) " Ss(t)) (11) 1 However, SCDP implements the instantaneous actual-arrival and actual-service curve instead of the actual-arrival and actual-service curve due to its implementation in wireless networks. The unpredictability and burstiness of traffic has to be taken into account and as such, B may not be as easily determined. Hence, a method to attain the buffer min requirements would be to run the simulation with a high and erratic load and get a rough

59 estimate of the buffer size needed. After implementation of the buffer size, packets exceeding the specified buffer threshold B would have to be discarded. min 4.3 Suggested improvements to SCDP It is noted that SCDP does provide a proportional differentiation on the delay violation ratios in wireless networks. However, when calculating the actual service curve, a timeshifted version of the actual arrival curve may not be sufficient for the calculation of the delay violation ratio as the traffic arrival of the incoming traffic may be erratic and as a result, the idealized service curve would also be erratic in the calculation of the delay violation ratio. With this, the Interpolated Service Curve Dynamic Priority (I-SCDP) is proposed. Similar to the concept of SCDP, the I-SCDP would use the concept of the instantaneous actual arrival and actual service curve. However, a bounded arrival curve and bounded service curve [18] [24] would be put in place to calculate the delay violation ratio. In this way, there would be a fixed parameter, the bounded service curve, to calculate our delay violation ratio.

60 Number of Packets A i s(t) rate r a i s(t) S i s(t) s i s(t) q i * t 1 * t 2 * t 2 t 1 Time "t "d Fig. 4.3: Illustration of I-SCDP Here, A i s(t) is the bounded arrival curve with rate r, S i s(t) is the bounded service curve, a i s(t) is the instantaneous actual arrival curve, s i s(t) would be the actual service curve, t 1 would be the time where packet s reaches the scheduler, t 1 * would be the ideal point on the bounded arrival curve, "t would be the difference in time between both arrival curves for packet s, t 2 would be the time where packet s is served and leaves the scheduler, t 2 * would be the ideal point on the bounded service curve whereby the packet s should be serviced and "d would be the delay violation of packet s.

61 Definition 4.5 [Arrival Curve] The bounded arrival curve for flow i, A i s(t) is defined as a non-decreasing, non-negative function such that A i s(t) " s i s (t) for any packet s entering the scheduler and being enqueued into the queue at time t. The bounded arrival curve implies that A i s(t) is the maximum number of packets that may arrive from flow I within the time duration. However, it is noted that in wireless networks, this is impossible due to the unpredictable traffic resources. Thus, a token bucket would have to be implemented to regulate the amount of traffic going into the scheduler. Definition 4.6 [Service Curve] The idealized service curve for flow i, S i s(t) is defined as a non-decreasing, non-negative function such that S i s(t)" s i s(t) for any packet leaving the scheduler and being dequeued into the outgoing link at time t. This would mean that s i s(t) would be the minimum amount of service provided to flow i within the time duration t. However, in view that I-SCDP is an extension of SCDP that implements the Difee performance specification in a wireless environment, S i s(t)" s i s(t) and (2) would be the precondition for delay violation to take place. Calculation of time of packet s served on the Service curve As it can be seen from the diagram, the motivation of I-SCDP would be to have a fixed parameter, the bounded service curve, to calculate our delay violation ratio. When packet s of flow i arrives at the scheduler, the time would be a i s. However, the actual time on the bounded service curve, S i s would required to calculate the delay violation ratio.

62 With this, interpolation is used to determine the point of which the packet s was to have arrived on the bounded arrival curve, A i s(t), and the bounded service curve, S i s(t), would be a time shifted value of the bounded service curve such that A i s(t - q i *). The bounded service curve, S i s(t), would have the rate r. Thus for packet s in flow i the difference in the bounded arrival and instantaneous arrival curve would be: "t = [ rt1# as(t) ] (12) r Time, t 2 *, on the bounded service curve which packet s would meet if the it meets up to the service guarantee provided to flow i: t 2* = [(t1 " #t) + qi*] (13) Delay violation of the packet s: "ds = t 2 # t 2 * (14) Delay violation ratio, "i of the packet would be thus similar to Definition 4.3:

63 "i = #ds qi * (15) Using (15) and manipulating it into (9), the concept of SCDP and I-SCDP would be similar. The proportional differentiation between the delay violation ratios of flow i and j would be defined as: "i = "j #i #j i, j"# Here, "i and "j would be the delay violation ratios, "i and "j would be the differentiation parameter of users i and j respectively. Thus, in selection of the packets to be serviced, packets with a lower delay violation ratio would be served with a greater priority as opposed to flows with a higher delay violation ratio. Hence, the steering parameter to determine which flow is to be serviced would be determined proportionally with "i = "j #i #j, with the goal of I-SCDP to provide a proportional differentiation on the delay violation ratios within the target inaccuracy bound, " # $i & $j %i %j. The advantages of I-SCDP would that it provides a fixed parameter, the bounded service curve, S i s(t), for the calculation of the delay violation ratio. This would guarantee a fixed

64 value of which the scheduler can use to determine which packet should be served. In addition, I-SCDP would also possess the advantages of SCDP over other wireless scheduling algorithms as mentioned in Section 4.1. The disadvantages of I-SCDP would be its medium implementation complexity as opposed to the simple implementation of SCDP. This would be due to the need to simulate another service curve, the bounded arrival curve, to determine the time on the bounded service curve, which would be the service guarantee provided to the packet. 4.4 Summary In view of meeting the Difee performance specification defined in Chapter 2, a novel wireless scheduling algorithm is proposed to provide a proportional differentiation on the delay violation probability of two independent flows. With this, this chapter focused on the motivation and contribution of SCDP, its advantages over the QoS wireless scheduling algorithms analyzed in Chapter 3 as well as the methodology of implementation of Difee into SCDP. SCDP was further evaluated and suggested improvements were made to it by guaranteeing a bounded service curve of which the delay violation ratio could be calculated. With this, I-SCDP was proposed to overcome the erratic behavior of the actual arrival curve in SCDP. With this, the thesis aims to provide a performance study on the proposed algorithms SCDP and I-SCDP to show that it conforms to the Difee performance specification.

65 Chapter 5: Implementation and Simulation This chapter describes the design, implementation and a performance study of the Service Curve Dynamic Priority (SCDP) and Interpolated Service Curve Dynamic Priority (I- SCDP) algorithm in a wireless network. The implementation and performance study stages would be subdivided into various sections, namely: 1) Design of SCDP; 2) Implementation of SCDP; 3) Simulation Results and Analysis of SCDP and I-SCDP; 4) Summary. 5.1 Design of SCDP The goal of SCDP was to create a scheduling algorithm for wireless networks that had a very low implementation effort, several desired qualities and to meet the Difee performance specification. The core of the scheduler would be in its ability to determine which front packet of the queue would be served first, thus, SCDP was designed to implicitly track the instantaneous actual arrival curves, idealized service curve and the actual service curves.

66 Two modules would be designed to carry out the scheduling algorithm, namely, the 1) Enqueuing Function and the 2) Dequeuing Function. The enqueuing function starts when the packet from the source has reached the scheduler and is enqueued into the buffer. The dequeuing function starts when the scheduler is idle and ready to choose which packet of the queues to be serviced. Flow charts of the two stated functions are as shown. Enqueuing Function Start Packets enter buffer, instantaneous arrival time, t 1, t 2 t i, is clocked. t 1, t 2 t i is recorded in an external linked list or data structure, i.e. Queue1 list<double>, etc. Packets are enqueued in its respective buffers. Packets wait to be serviced when they are at the head of queue and Dequeue function is called Fig. 5.1: Flow Chart of Enqueue Function

67 (2) Dequeuing Function Start Scheduler is idle, calls dequeuing function Instantaneous time when scheduler is idle is recorded. Linked lists or data structure which holds the instantaneous arrival times for packets of different flows are Idealized Service time, delay violation ratio is calculated. Packets of flows with a smaller delay violation ratio in proportion to each other are serviced first. Linked lists pushes out instantaneous arrival time of packet which has been serviced. Scheduler becomes idle until next Dequeuing function is called. Fig. 5.2: Flow Chart of Dequeue Function

68 5.2 Implementation of SCDP Implementation of SCDP is done in NS-2 [25] [26] [27], a network simulator developed by the University of Berkeley. NS-2 requires both C++ and TCL Scripting. The scheduling algorithm was ported into the DiffServ modules of NS-2, and the enqueuing and dequeuing functions are as follows: /* Function : SCPQenqueue() Description : updates the timing of the packets which reach the queue, inserts timing data into the lists,invokes sceduling of next departure. Changes : Input : int queueid Output : */ void dsredqueue:: SCPQenqueue(int eq_id) { //initialises packet & queueid double instantaneous_time0, instantaneous_time1; double now = Scheduler::instance().clock(); instantaneous_time0=now; instantaneous_time1=now; //inserts timing of instantaneous packet arrival into linked lists if (eq_id == 0) { Queue0.push_back(instantaneous_time0); else if (eq_id == 1) { Queue1.push_back(instantaneous_time1); Fig. 5.3: Code of Enqueue Function in ns-2

69 /* Function : SCPQdequeue() Description : is invoked by the scheduler, dequeues packets accordingly. Changes : Input : Output : */ void dsredqueue::scpqdequeue () { double delay_bound0, delay_bound1; double idealized_service0, idealized_service1; double delay_violation_ratio0, delay_violation_ratio1; double service_time; delay_bound0 = 0.002; //delay bound delay_bound1 = 0.002; double now = Scheduler::instance().clock(); service_time = now; //Calculate respective proportional differences idealized_service0 = delay_bound0 + Queue0.front(); idealized_service1 = delay_bound1 + Queue1.front(); delay_violation_ratio0 = (service_time - idealized_service0)/delay_bound0; delay_violation_ratio1 = (service_time - idealized_service1)/delay_bound1; if (delay_violation_ratio0 > delay_violation_ratio1) { qtodq = 0; Queue0.pop_front(); else if (delay_violation_ratio0 < delay_violation_ratio1) { qtodq = 1; Queue1.pop_front(); Fig. 5.4: Code of Enqueue Function in ns-2 Further use how of file streams and linked lists and how the functions are called can be found in the Appendix A.

70 The traffic conditions are also changed in the *.tcl files of ns-2. In the case for the implementation of SCDP in a wireless scenario, CBR traffic, Exponential Traffic and Pareto Traffic are used to simulate the algorithm. How the traffic conditions are changed are as follows: set traffic0 [new Application/Traffic/CBR] $traffic0 set rate_ 100k Fig. 5.5: Code of setting CBR traffic set traffic0 [new Application/Traffic/Exponential] $traffic0 set PacketSize_ 1000 $traffic0 set burst_time_ 250ms $traffic0 set idle_time_ 500ms $traffic0 set rate_ 100k Fig. 5.6: Code of setting Exponential traffic set traffic0 [new Application/Traffic/Pareto] $traffic0 set PacketSize_ 1000 $traffic0 set burst_time_ 250ms $traffic0 set idle_time_ 500ms $traffic0 set rate_ 100k $traffic0 set shape_ 1.5 Fig. 5.7: Code of setting Pareto traffic With this knowledge of ns-2, the simulation and performance study of SCDP over ns-2 is conducted.

71 5.3 Simulation Results and Analysis SCDP and I-SCDP was simulated under various traffic conditions, namely: 1) CBR Constant Bit Rate; 2) Exponential Traffic; 3) Pareto Traffic. Constant Bit rate traffic is used for debugging purposes for several reasons, one would be to understand how scheduler would perform when the scheduler is overloaded and the capacity of the outgoing links are lower than that of the incoming links. Exponential and Pareto Traffic simulates the wireless scenario by setting the packet burst times and idle times for the source to be on and off. Thus, packets are sent according to the preset distribution during on periods and no packets are sent during off periods. This is to mimic a wireless network, whereby there might be bursty traffic in some instances and none in other durations. (1) Constant Bit Rate (CBR) traffic - SCDP The CBR traffic generator in ns-2 generates traffic according to a deterministic rate. Packets are constant size and set to For debugging purposes, CBR traffic was used and the incoming links were overloaded. This would be the worst-case scenario in a wireless network whereby " R > C j, where the summation of all the rates of incoming traffic would be greater than the channel capacity for delay violation to occur. The buffer capacity is set to infinity to prevent any drop of packets; the buffer can be changed later upon analysis of the instantaneous arrival

72 and service curves. In a wireless network, the amount of packets entering the scheduler would be lesser and in on/off bursts, thus, CBR is only used to gauge the performance of the algorithm in a worst-case scenario. The outgoing capacity is set to 5Mb/s; thus, the ingoing links of the flow flows into the scheduler are set at 3Mb/s and 3.5Mb/s to test the scheduler. The instantaneous actual-arrival curves, idealized service curves and actual-service curves can be seen as illustrated in the diagrams below. Queue 0: CBR traffic, Rate = 3Mb/s Number of packets Time/s Actual Arrival Idealized Service Actual Service Fig. 5.8: Instantaneous curves of Queue 0 with CBR traffic when rate = 3Mb/s

73 Queue 1: CBR traffic, Rate = 3Mb/s Number of packets Time/s Actual Arrival Idealized Service Actual Service Fig. 5.9: Instantaneous curves of Queue 1 with CBR traffic when rate = 3Mb/s Delay Violation Ratios Ratio of Q0/Q Delay Violation Ratios Number of Packets Fig. 5.10: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3Mb/s

74 Queue 0: CBR traffic, Rate = 3.5Mb/s Number of Packets Time/s Actual Arrival Idealized Service Actual Service Fig. 5.11: Instantaneous curves of Queue 0 with CBR traffic when rate = 3.5Mb/s Queue 1: CBR traffic, Rate = 3.5Mb/s Number of packets Time/s Actual Arrival Idealized Service Actual Service Fig. 5.12: Instantaneous curves of Queue 1 with CBR traffic when rate = 3.5Mb/s

75 Delay Violation Ratios 4 2 Ratio of Q0/Q Delay Violation Ratios Number of Packets Fig. 5.13: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3.5Mb/s It can be seen that from equation (9) in Chapter 4 that to meet the Difee performance specification, the flows must be proportionally differentiated with each other such that: "i = "j #i #j Whereby "i and "j are the delay violation ratios of flow i and flow j respectively, "i and "j are the differentiating parameter of flow i and j respectively which determines how much service is to be given to that particular flow. By manipulation of the equation, it can be seen that: "i = #i "j #j (16)

76 In the simulation throughout this thesis, the ratio "i "j has been set to a 1:1 ratio, such that both flows would expect equal service. However, in differentiation and allocating different priorities to different flows, "i "j can be set to whichever specifications that the end-user requires. As mentioned, the ideal case to meet the Difee performance specification would be such that the target inaccuracy be as small as possible, which in the ideal case would be "i $ "j = 0. However, this is impossible in a real-time scenario, thus, the objective #i #j would be for SCDP to fall within a target inaccuracy bound such that " # $i & $j %i %j, i.e. the highest tolerable accuracy in the proportional differentiation of the packet drop ratios. As it can be seen from the simulation results, the ratios of the two independent delay violation ratios fall within the specified target inaccuracy bound. Whereby "i "j is set to 1, "i "j fluctuates very closely around 1. The graph is further emphasized in Fig and 5.15, whereby the fluctuation of the delay violation ratios can be seen more clearly as not a straight line, but as values which fluctuate around 1. The output graphs are similar to that of a control system, whereby the system initially goes into a transient state, after which it goes into a steady state.

77 When the scheduler is overloaded further with each independent flow having a rate of 3.5Mb/s into the scheduler, it can be seen that although the instantaneous actual-arrival and the actual-service curve are further away from each other due to larger amount of packets being enqueued into the buffer and having a longer period to be serviced. The output of the scheduling algorithm still falls within the desired output of falling within the specified target inaccuracy bound. Further simulations are then done using other traffic generators to test for the viability of the scheduling algorithm. (2) Exponential Traffic SCDP Exponential traffic generates traffic according to an Exponential On/Off distribution. Packets are sent at a fixed rate during on periods, and no packets are sent during off periods. Packets are of constant size and set to Both on and off periods are taken from an exponential distribution. The Exponential Traffic mimics a wireless network whereby there might be periods of burstiness as well as idle periods. Simulations are done using exponential traffic as the traffic generator. The burst time whereby the traffic is being sent into the scheduler is varied and tested for 100ms, 200ms and 500ms. The idle time of the traffic generator is set to 5ms. The simulation results of wireless traffic having an Exponential distribution are as follow:

78 Delay Violation Ratios Ratio of Q0/Q Delay Violation Ratios Number of Packets Fig. 5.14: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 100ms, Idle Time = 5ms Delay Violation Ratios Ratio of Q0/Q Delay Violation Ratios Number of Packets Fig. 5.15: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 200ms, Idle Time = 5ms

79 Delay violation Ratios Ratio of Q0/Q Delay violation Ratios Number of packets Fig. 5.16: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 500ms, Idle Time = 5ms (3) Pareto Traffic SCDP Pareto traffic generates traffic according to a Pareto On/Off distribution. Packets are sent at a fixed rate during on periods, and no packets are sent during off periods. Packets are of constant size and set to Both on and off periods are taken from a Pareto distribution specified by the user. Here the burst time of the traffic generator is set to 100ms, the idle time set to 5ms and the Pareto shape set to 1.5.

80 Delay Violation Ratios Ratio of Q0/Q Delay Violation Ratios Number of Packets Fig. 5.17: Proportional Delay violation Ratios of q 0 /q 1 of Pareto Traffic, Burst Time = 100ms, Idle Time = 5ms, Pareto Shape = 1.5 As it can be seen from the above graphs, that for all the simulations for the three different traffic generators, the ratios of the two independent delay violation ratios fall within the specified target inaccuracy bound. Thus, this would mean that the Service Curve Dynamic Priority (SCDP) algorithm is able fulfill the Difee performance specification as it can provide differentiation directly on the delay violation ratios. Similarly, I-SCDP is simulated for CBR, Exponential and Pareto traffic.

81 (4) CBR Traffic I-SCDP Fig. 5.18: Proportional delay violation ratios of q 0 /q 1 of CBR, rate = 3Mb/s (I-SCDP)

82 (5) Exponential Traffic I-SCDP Fig. 5.19: Proportional delay violation ratios of q 0 /q 1 of Exponential Traffic, Burst Time = 100ms, Idle Time = 5ms (I SCDP)