Fair Class-Based Downlink Scheduling with Revenue Considerations in Next Generation Broadband wireless Access Systems Bader Al-Manthari, Member, IEEE, Hossam Hassanein, Senior Member, IEEE, Najah Abu Ali, Member, IEEE, and Nidal Nasser, Member, IEEE Presented by Reghu Anguswamy and Fenye Bao 1

Outline Introduction Related work System model Proposed scheme – Opportunity cost & utility function – Scheduling different types of traffic Performance evaluation Conclusion 2

Introduction Broadband Wireless Access Systems (BWAS) – High Speed Downlink Packet Access (HSDPA) – (WiMAX) QoS Provisioning – Satisfying diverse QoS requirements of users while maximizing revenues of network operators 3

Levels of QoS Provisioning 4

Design Issues (1) Channel quality conditions – Varying data rates due to mobility – Selection of users with good channel quality conditions to maximize system capacity Fairness – User with bad channel quality conditions may not get served and suffer from starvation 5

Design Issues (2) QoS requirements – Different QoS for different applications Revenue losses – Loss of revenue due to scheduling low-revenue generating users – Buffering data at base station – Pay for services 6

Packet Scheduling Nonreal-time – Main QoS metric is average throughputs – Maximum Carrier to Interference Ratio (Max CIR) Chooses users best quality channel conditions maximizing system capacity at the expense of fairness – Proportional Fairness (PF) Balances capacity-fairness tradeoff by serving users with best relative channel quality Only fair when similar channel conditions – Score-Based (SB) Scheduling scheme – Fast Fair Throughput (FFT) 7

Packet Scheduling Real-time – Designed for multimedia traffic with various QoS requirements such as min. data rate or max. delay – Buffering data at base station – Pay for services – Modified Largest Weighted Delay First (M-LWDF) and Fair M-LWDF (FM-LWDF) – Max CIR with Early Delay Notifications(EDN) 8

Packet Scheduling Real-time – Modified Largest Weighted Delay First (M-LWDF) and Fair M-LWDF (FM-LWDF) Relative channel quality multiplies by a term representing user’s packet delay FM-LWDF borrows equalizer term from FFT scheme and adds it to M-LWDF to improve fairness – Max CIR with Early Delay Notifications(EDN) Scheduling using Max CIR as long as packet delays are below a certain threshold 9

Contributions of the Paper Supporting multiple classes of service for users having different QoS and traffic demands. Satisfying the conflicting requirements of the users and network operators. Maximizing the throughput of the wireless system. Ensuring a fair distribution of wireless resources. 10

Two Economics Concepts Opportunity Cost – The opportunity cost for a good is defined as the value of any other goods or services that a person must give up in order to product or get that good. – For example: if a gardener can grow carrots or potatoes, the opportunity cost of growing carrots is the alternative crop (potatoes) might have been grown instead. 11

Two Economics Concepts Law of Diminishing Marginal Utility – increasing amounts of a good or of a service are consumed, past some point of consumption the utility (usefulness) of successive increases drops. 12

System Model and Packet Scheduling Model 13

System Model and Packet Scheduling Model Downlink transmission is done in time frames. Each frame consists of a number of time slots. The base station serves N users. There are K classes of traffic. Each class (i) of traffic has N i users: N = N 1 + … + N K, (1 <= i <= K) Each user regularly informs the base station of his channel quality condition through uplink. 14

Fair Class-Based Packet Scheduling General Formulation – Utility function to represent the satisfactions of mobile users. – Opportunity cost function to represent the cost of serving them. 15

Fair Class-Based Packet Scheduling Objective: Subject to: is the set of users (represented by ) selected to transmit to. 16 Utility function QoS measures Lower and upper bounds on QoS Data rate constraint Opportunity cost constraint NiNi

The Opportunity Cost The opportunity cost is defined as follows: : price per bit for user j in class i; : the revenue earned if user j is served. : the maximum obtainable revenue in current time frame. 17

The Opportunity Cost Revenue loss constraint: H = 0, cannot tolerate any revenue loss; H = 0.2 Rev Max, restricts the revenue loss to be no more than 20% of the maximum obtainable revenue; H = Rev Max, the opportunity cost is ignored. 18

The Opportunity Cost To guarantee certain levels of QoS (real-time traffic), the opportunity cost constraint may be relaxed: Where: is the obtainable revenue from the users that require QoS guarantees. 19 Reserved to guarantee QoS

The Utility Function The authors introduced following utility function: Properties of this utility function – Nondecreasing – Diminishing marginal utility – Prioritizing different classes (larger a i increases the slope of utility function?) 20

The Utility Function Objective: Scheduling decision: – Computing the aggregate utility if only user (i,j) is scheduled and all others are not. – Finding the set of users with highest aggregate utility (in descending order). – Optimal? Approximately optimal? 0-1 knapsack. DP! 21 Utility of served users Utility of not served users

Scheduling Different Types of Traffic QoS Measures – Average throughput – Minimum data rate requirements – Maximum delay requirements QoS measures are chosen so that the scheduling scheme achieves the objectives 22

Scheduling Different Types of Traffic Exploiting the user channel quality conditions in scheduling decisions Maximizing the users individual data rates and the system throughput Users with good channel quality conditions will have higher priority to transmit 23

Scheduling Different Types of Traffic 24

Scheduling Different Types of Traffic - different definitions for different traffic types e – best effort traffic – Increases fairness measure of a user – if a user with high average throughput is served, though his utility will increase, the social welfare of the system will not be maximized because of the rapid decrease of utilities of users with low average throughputs as a result of the diminishing marginal property 25

Scheduling Different Types of Traffic r – minimum data rate requirements represents a fairness measure – if the user is receiving a low average throughput compared to other users who request the same data rate, the rate of decrease in his utility function will be higher than the other users – Scheduler will be forced to serve the user to increase his utility, and hence, maximize the social welfare of the system. 26

Scheduling Different Types of Traffic d – maximum delay requirements - also represents fairness measure similar to the case for minimum data rate requirements 27

Scheduling Different Types of Traffic 28

Scheduling Different Types of Traffic 29

Performance Evaluation Traffic Models – VoIP (class 1) – Audio streaming (class 2) – Video streaming (class 2) – FTP (class 3) Priority: class 1 > class 2 > class 3, audio > video. Price: p ij = 6, 4, 2, 1 for VoIP, audio, video, FTP. 30

Performance Evaluation Channel Model – The channel condition changes with time depending on the user’s environment and speed. – The channel models consists of 5 parts: Distance loss Shadowing Multipath fading Intracell interference Intercell interference 31

Performance Evaluation Simulation Results – One traffic type VoIP and audio streaming (real-time) – Compared with M-LWDF, FM-LWDF, Max CIR, and EDN. Video streaming and FTP (nonreal-time) – Compared with CIR, PF, and FFT. – Multiplexed scenario Shows the ability to provide inter and intraclass prioritization. 32

Performance Evaluation Average packet delay (VoIP) 33

Performance Evaluation Average packet delay of FCBPS with different revenue losses (VoIP) 34

Performance Evaluation The Jain fairness index (VoIP) 35

Performance Evaluation The Jain fairness index of FCBPS with different revenue losses (VoIP) 36

Performance Evaluation Average throughput (video streaming) 37

Performance Evaluation The Jain fairness index (video streaming) 38

Performance Evaluation Percentage of service coverage for all traffic types (multiplexed traffic) 39

Performance Evaluation The Jain fairness index for all traffic types (multiplexed traffic) 40

Conclusion The proposed scheduling scheme: utilizes utility and opportunity cost functions – to satisfy the QoS requirements of users – under the revenue loss constriction of network operators. supports both interclass and intraclass prioritization. ensures fairness at packet-level. 41

Questions? 42