Distributed Fair Scheduling in a Wireless LAN Gautam Kulkarni EE206A (Spring 2001) Nitin Vaidya, Paramvir Bahl and Seema Gupta (appeared in Mobicom 2000 Boston, MA)

Introduction Requirements of a scheduling discipline: –Ease of implementation –Fairness and protection –Performance bounds –Ease of admission control (if needed) With fair scheduling bandwidth for a flow  weight MAC is not fair How to introduce fairness in wireless LANs ?

Fair Queueing “Ideal” scheduling discipline – Generalized Processor Sharing (GPS) All fair queueing disciplines try to emulate GPS Traditional GPS-like disciplines centralized in design Previous work on fairness in distributed MAC protocols: –Limited in scope – provide equal bandwidth share (e.g. MACAW) –Suffer in the presence of location-dependent errors

Fair Scheduling Distributed Fair Scheduling (DFS) – new protocol for fair scheduling A distributed algorithm derived from the Distributed Coordination Function (DCF) in Emulation of Self-Clocked Fair Queueing (SCFQ) in a distributed manner Scheduler maintains a “virtual clock” to keep track of packets to be serviced

SCFQ Main idea: Start tag of packet Finish tag of packet V(0) = 0. Virtual time = finish tag of packet in service Transmit packet with smallest finish tag Packets stamped on reaching the head of the queue

Distributed Coordination Function CSMA/CA Node i chooses backoff interval = B i slots B i uniformly distributed in [0, cw] where cw = size of contention window Decrement B i Is B i == 0 ? –Yes: Send RTS Receive CTS –No CTS ? Double cw, select new B i and repeat from start Send data Receive ACK –No: Decrement B i

Distributed Fair Scheduling (DFS) Protocol Marriage of a distributed version of SCFQ with DCF Key idea – select backoff interval proportional to the finish tag of the packet to be transmitted Each node maintains a local virtual clock v i (t) Backoff interval = Scaling_Factor * length / weight * random number with mean 1

DFS (contd.) Collision handling –To reduce “priority” reversals, a small backoff interval is chosen after the first collision –Backoff interval increased exponentially on further collisions Potential drawbacks –Can exhibit short-term unfairness –Impact of small weights of backlogged flows

Impact of Small Weights Recall: Backoff intervals are being used to compare “length/weight” Small weights can lead to high idle times – throughput degradation Intuition: Any non-decreasing function of length/weight may be used to obtain backoff intervals Need to explore alternate mappings

Alternate Mappings Chosen backoff interval Scaling_factor * length / weight * random number

Alternate Mappings (contd.) Advantage –smaller backoff intervals –less time wasted in counting down when weights of all backlogged flows are small Disadvantage –backoff intervals that are different on a linear scale may become identical on the compressed scale –possibility for greater number of collisions

Performance Evaluation Using modified ns-2 simulator: 2 Mbps channel Number of nodes = N Number of flows = N/2 Odd-numbered nodes are destinations, even-numbered nodes are sources Unless otherwise specified: –flow weight = 1 / number of flows –backlogged flows with packet size 584 bytes (including UDP/IP headers) –Scaling_Factor = 0.02

Fairness Index Fairness measured as a function of (throughput T / weight  ) for each flow f over an interval of time –Unless specified, the interval is 6 seconds

Throughput/Weight Variation across Flows Through put / Weight Flow destination identifier Flatter curve is fairer DFS is fairer

Throughput-Fairness Tradeoff Fairness index Number of flows

Throughput-Fairness Tradeoff Aggregate throughput (all flows combined) Number of flows

Scaled Fairness of can be improved by using larger backoff intervals Is DFS fairer simply because it uses large backoff intervals ? Scaled = which uses backoff interval range comparable with DFS

Short Term Fairness Frequency Number of packets transmitted by a flow (over 0.04 second windows) Narrow distribution is fairer DFS is fairer

Fairness Versus Sampling Interval Size Fairness Index Interval Size

Scaling Factor How to select the scaling factor ? –Small number : May result in more collisions –Large number: Larger overhead

Impact of Scaling Factor Fairness Index Scaling Factor six flows with weights 1/2,1/4,1/8,1/16,1/32,1/32

Impact of Scaling Factor Scaling Factor Aggregate Throughput six flows with weights 1/2,1/4,1/8,1/16,1/32,1/32

Conclusions DFS improves fairness compared to and Scaled Alternative mappings somewhat beneficial No distributed fair scheduling protocol may accurately emulate work-conserving centralized protocols (unless clocks are synchronized)

The Mandatory Critique! Need to evaluate the effect of collision resolution mechanisms to maintain priorities Selection of scaling factor could be adaptive Actually, a very good paper! The End Acknowledgements: I have borrowed some slides from Prof. Vaidya’s webpage.