Fair Real-time Traffic Scheduling over A Wireless Local Area Network Maria Adamou, Sanjeev Khanna, Insup Lee, Insik Shin, and Shiyu Zhou Dept. of Computer & Information Science University of Pennsylvania, USA

2 Real-time Communication over Wireless LAN BS MH1 MH3 MH2

3 Wireless LAN MAC Protocol  IEEE – standard DCF (distributed)  Contention-based transmission PCF (centralized)  Contention-free (CF) transmission  BS schedules CF transmissions by polling

4 Wireless Network Characteristics  Unpredictable Channel Error location dependent bursty BS MH1 MH3 MH2 

5 Challenges  How do channel errors affect real-time transmissions? QoS degradation Wireless channel error model  How does BS schedule real-time transmissions with unpredictable errors? Real-time scheduling objective considering QoS degradation with errors Real-time scheduling algorithm

6 Outlines  Real-time traffic model  Scheduling objectives  Theoretical results  Online scheduling algorithms  Simulation results  Conclusion

7 Real-time Traffic Model  Periodic packet generation (release time)  Soft deadline Upon missing deadline, a packet is dropped  Acceptable packet loss (deadline miss) rate Degradation = actual loss rate – acceptable loss rate  The same packet length (execution time)

8 Scheduling objectives 1. Fairness (considering each flow) Location dependent channel errors Minimizing the maximum degradation 2. Throughput (considering the system) Maximizing the overall system throughput (fraction of packets meeting deadlines)  Online scheduling algorithm without knowledge of error in advance

9 Theoretical results  No online optimal algorithm Performance ratio of an online algorithm w.r.t. optimal  for throughput maximization, two  for achieving fairness, unbounded  For the combined objectives, unbounded  A polynomial time offline algorithm that optimally achieves our scheduling objectives

10 Online scheduling algorithms  EDF (Earliest Deadline First)  GDF (Greatest Degradation First)  EOG (EDF or GDF)  LFF (Lagging Flows First)

11 EDF (Earliest Deadline First) when a new packet is available DiDi εiεi EDF Queue Scheduler when it dispatches

12 GDF (Greatest Degradation First) when a new packet is available DiDi εiεi GDF Queue Scheduler when it dispatches

13 EOG (EDF or GDF) when a new packet is available EDF Queue Scheduler when it dispatches GDF Queue If there is a packet that will miss its deadline after next slot Otherwise

14 LFF (Lagging Flows First) when a new packet is available DiDi εiεi LFF Array 4 index

15 LFF (Lagging Flows First) when a new packet is available DiDi εiεi LFF Array Scheduler when it dispatches 4 index

16 LFF (Lagging Flows First) when a new packet is available EDF Queue Scheduler when it dispatches GDF Queue If there is a packet that will miss its deadline after next slot Otherwise

17 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

18 Simulation – Error Modeling  Random blackouts (w i ) for error period  Error duration rate = BS MH1 MH3 MH2  MH1  t max t0t0 MH2  MH3  wiwi

19 Results – Max Degradation

20 Results – Throughput Ratio

21 Related Work  QoS guarantees over wireless links No consideration of fairness issue  WFQ over wireless networks No consideration of deadline constraint  QoS degradation considering deadline Imprecise computation IRIS ( Increased Reward with Increased Service ) (m,k)-firm deadline model DWCS ( Dynamic Window-Constrained Scheduling )

22 Conclusion  Scheduling objectives 1. Fairness – minimizing the maximum degradation 2. Overall throughput maximization  Theoretical results No online algorithm can be guaranteed to achieve a bounded performance ratio for the scheduling objective

23 Conclusion  Online algorithms For fairness objective 1. LFF2. GDF3. EOG4.EDF For maximum throughput objective 1. EDF2. LFF3. EOG4.GDF Future work Variable length packets Other measures of fairness