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Fair Real-time Traffic Scheduling over A Wireless Local Area Network

Fair Real-time Traffic Scheduling over A Wireless Local Area Network. M aria Adamou, S anjeev Khanna, I nsup Lee, Insik Shin, and S hiyu Zhou Dept. of Computer & Information Science University of Pennsylvania , USA. Real- t ime Communication over Wireless LAN. MH 1. BS. MH 2.

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Fair Real-time Traffic Scheduling over A Wireless Local Area Network

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  1. 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 MH1 BS MH2 MH3

  3. IEEE 802.11 – standard DCF (distributed) Contention-based transmission PCF (centralized) Contention-free (CF) transmission BS schedules CF transmissions by polling Wireless LAN MAC Protocol

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

  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. εi Di 0.2 0.4 0.3 0.1 3 4 3 1 EDF Queue EDF (Earliest Deadline First) when a new packet is available when it dispatches Scheduler

  12. εi Di 0.2 0.1 0.3 0.4 3 1 3 4 GDF Queue GDF (Greatest Degradation First) when a new packet is available when it dispatches Scheduler

  13. 0.2 0.4 0.3 0.1 0.4 0.3 0.1 3 1 4 1 3 4 3 EDF Queue GDF Queue EOG (EDF or GDF) when a new packet is available If there is a packet that will miss its deadline after next slot when it dispatches Scheduler Otherwise

  14. εi Di 0.2 3 0.4 0.3 0.1 1 3 4 LFF (Lagging Flows First) when a new packet is available index 4 3 2 1 LFF Array

  15. εi Di 0.2 3 0.2 0.3 0.4 0.1 1 3 4 3 LFF (Lagging Flows First) when a new packet is available index 4 3 2 when it dispatches 1 Scheduler LFF Array

  16. 0.2 0.4 0.3 0.1 0.4 0.3 0.1 2 1 4 1 2 4 3 EDF Queue GDF Queue LFF (Lagging Flows First) when a new packet is available If there is a packet that will miss its deadline after next slot when it dispatches Scheduler Otherwise

  17. Simulation – Performance Metrics • Degradation (for each flow) • Fraction ofpackets lostbeyond the acceptable packet loss rate • Throughput (over all flows) • Fraction of successfully transmitted packets

  18.  MH1 Simulation – Error Modeling • Random blackouts (wi) for error period • Error duration rate = t0 tmax wi MH1 BS MH2    MH3 MH2 MH3

  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 • Fairness – minimizing the maximum degradation • 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

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