Fair Real-time Traffic Scheduling over Wireless Local Area Networks Insik Shin Joint work with M. Adamou, S. Khanna, I. Lee, and S. Zhou Dept. of Computer & Information Science University of Pennsylvania

Real-Time Packet Scheduling Real-Time Flow –Periodic interval interval between arrival time of two packets –Deadline a packet should be scheduled and successfully transmitted within the time, otherwise it is lost –Acceptable packet loss rate degradation = actual loss rate – acceptable loss rate

Scheduling over Wireless LAN Cellular Wireless Network – one base station (BS) – multiple mobile hosts (MHs) – BS schedules real-time packet transmissions of BS & MHs using polling mechanism BS MH

Scheduling over Wireless LAN Cellular Wireless Network – Unpredictable channel error location dependent bursty BS MH1 MH3 MH2  

Scheduling Motivation Unpredictable wireless channel error –failure of packet delivery in time –degraded quality of service –some flows may have more degraded QoS while others may have less degraded QoS, due to location dependent property Fair scheduling of real-time packets with deadlines in the presence of the errors

Previous Work QoS guarantees over wireless links –No consideration of fairness issue WFQ over wireless networks –No consideration of deadline constraint (m,k)-firm deadline model –should meet deadlines of m out of k consecutive packets –Similar to our deadline model, except that we consider fair degradation without any guarantees in wireless network (unpredictable error can violate any guarantee)

Scheduling Objectives 1.Achieving fairness by minimizing the maximum degradation among all flows 2.maximizing the overall system throughput simultaneously Online scheduling algorithm –without knowledge of error in advance

Theoretical Results No online optimal algorithm for our scheduling objectives –for throughput maximization, an online algorithm can achieve a performance ratio of two w.r.t. the optimal –for achieving fairness, no online algorithm can guarantee a bounded performance ratio w.r.t. optimal –Hence, none can guarantee a bounded performance ratio w.r.t. optimal for the combined objectives A polynomial time offline algorithm that optimally achieves our scheduling objectives

Online Scheduling Algorithms EDF (Earliest Deadline First) –Naturally suited for maximizing throughput GDF (Greatest Degradation First) –Seeks to minimizing the maximum degradation EOG (EDF or GDF) –Simply combines EDF and GDF LFF (Lagging Flows First) –Favors lagging flows (receiving degraded QoS) in a more clever, sophisticated manner

Online Algorithm - LFF LFF (Lagging Flows First) –Try to schedule the k most lagging flows when at most k flows can be scheduled in the next available slots.

Scheduling Example available for scheduleNOT available for schedule slot schedule ? ? ? ? Flow  i slot Assume that  i decreases by   upon a successful transmission of a packet of flow i and increases by   upon a failure of a packet, where 0.05 <   < 0.1  i – degradation degree of flow i

Scheduling Example  scheduled Flow  i   slot    slot schedule EDF schedule  i                max :    schedule 4 packets

Scheduling Example  scheduled Flow  i   slot   slot schedule  GDF schedule  i               GDF – Greatest Degradation First   max :    schedule 3 packets

Scheduling Example  scheduled Flow  i slot   slot schedule EOG schedule  i               EOG– EOF or GDF   max :    schedule 4 packets  

Scheduling Example  scheduled Flow  i    slot  slot schedule LFF schedule  i                max :    schedule 4 packets

Error Handling Mechanisms Re-scheduling Mechanisms 1.No re-scheduling - dropping packets with errors 2.Immediate re-scheduling - ignoring errors 3.Delayed re-scheduling –How long does it need to delay? –Backoff value = deadline/2

Simulation Performance Metrics 1.Degradation (for each flow) –Fraction of packets lost beyond the acceptable packet loss rate 2.Throughput (over all flows) –Fraction of successfully transmitted packets

Results – Max Degradation

Results – Throughput Ratio

Conclusion Our scheduling objectives 1.Fairness – minimizing the maximum degradation 2.Overall throughput maximization Our theoretical study showed that no online algorithm can be guaranteed to achieve a bounded performance ratio for fairness objective For fairness objective 1. LFF 2. GDF 3. EOG 4.EDF For maximum throughput objective 1. EDF 2. LFF 3. EOG 4.GDF