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HCF and EDCF Simulations

HCF and EDCF Simulations. Aman Singla Greg Chesson Atheros Communications, Inc. Overview. Simulations First simulation results for both contention-based (EDCF) and polling-based (HCF) channel access methods Study the performance and efficiency of the two methods under the same scenarios

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HCF and EDCF Simulations

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  1. HCF and EDCF Simulations Aman Singla Greg Chesson Atheros Communications, Inc. Aman Singla, Atheros

  2. Overview • Simulations • First simulation results for both contention-based (EDCF) and polling-based (HCF) channel access methods • Study the performance and efficiency of the two methods under the same scenarios • Analysis • Understand the operational behavior of EDCF and HCF • Summary and Implications Aman Singla, Atheros

  3. Simulations Aman Singla, Atheros

  4. Background • Down-link characteristics are equivalent • EAP (HC) always has preferential access _ EAP can transmit at pifs for both EDCF / HCF • Concentrate on the up-link differences • EDCF: contention-based access • HCF: contention-free polled access • Concentrate on guaranteed service scenarios • Admission Control for all QoS streams • Uniform 10% PER for all frames • Channel degrades rapidly above 10% PER to make PHY rate backoff more effective Aman Singla, Atheros

  5. Background (continued) • Overload the channel with background traffic • best-effort traffic is not admission controlled • A realistic, non-trivial HCF scheduler • Uses all available information • Reacts within sifs to data arrival and queue state information • Implemented various heuristics and used the best performing • Report results for a 36Mb/s 11a PHY • Statistical principles apply at all PHY rates Aman Singla, Atheros

  6. Simulations • Scenario I Study the effect of number of QoS stations on latency and jitter • Scenario II Study the QoS capacity and efficiency of the two channel access methods • Scenario III Telephony case study Aman Singla, Atheros

  7. QoS Streams - Traffic Model • CBR streams with the inter-packet arrival interval based on a normal distribution around a period variance = period/4 mean = period Aman Singla, Atheros

  8. Simulations – I (constant load) Scenario • PHY = 36 Mb/s (11a), 10% PER • Background traffic 3 backlogged queues (ESTAs) @ 1500 Byte packets • QoS traffic Fixed 16.384 Mb/s load @ 2048 Byte packets Experiment • Vary the # of ESTAs applying the QoS load 2 streams (ESTAs) @ 2 ms period, 4/8/16 streams (ESTAs) @ 4/8/16 ms period, 32 streams (ESTAs) @ 32 ms period Objective • Study effect of changing number of ESTAs on end-to-end latency and jitter for QoS traffic Aman Singla, Atheros

  9. EDCF – Latency Distribution 80% of packets have end-to-end latency <= 2.5ms Aman Singla, Atheros

  10. HCF – Latency Distribution 2 streams 32 streams Aman Singla, Atheros

  11. Conclusions – Simulations I • EDCF is resilient to the number of stations applying the QoS load • This constant QoS load study shows that collision rate is largely independent of the number of stations • HCF scheduler phase contributes significantly to end-to-end latency • Scheduler phase: timing difference between poll arrival and data arrival Aman Singla, Atheros

  12. Simulations – II (varying load) Scenario • PHY = 36 Mb/s (11a), 10% PER • Background traffic 3 backlogged queues (ESTAs) @ 1500 Byte packets • QoS traffic 4 streams (ESTAs) CWMin/Max for EDCF = 7/15 Experiment • Vary the packet size and period (applied load) of the QoS traffic a) 4 x 2048 Bytes @ 3.5 ms b) 4 x 1500 Bytes @ 2.7 ms c) 4 x 1024 Bytes @ 2.1 ms d) 4 x 512 Bytes @ 1.4 ms e) 4 x 256 Bytes @ 1.1 ms f ) 4 x 128 Bytes @ 0.9 ms Objective • Compare channel capacity and efficiency for EDCF/HCF at similar performance levels Aman Singla, Atheros

  13. Latency Distribution (2048 Byte pkts) Aman Singla, Atheros

  14. Latency Distribution (1500 Byte pkts) Aman Singla, Atheros

  15. Latency Distribution (1024 Byte pkts) Aman Singla, Atheros

  16. Latency Distribution (512 Byte pkts) Aman Singla, Atheros

  17. Latency Distribution (256 Byte pkts) Aman Singla, Atheros

  18. Latency Distribution (128 Byte pkts) Aman Singla, Atheros

  19. Efficiency QoS load for both EDCF / HCF Efficiency is measured by the amount of background load supported Background load using EDCF Background load using HCF Aman Singla, Atheros

  20. Conclusions – Simulations II • EDCF and HCF demonstrate comparable latency and jitter performance • EDCF and HCF support similar levels of background traffic (are equally efficient) in the presence of the same QoS load for these case studies • EDCF and HCF have similar QoS capacity • QoS capacity is the maximal QoS load that can be supported for a particular set of service guarantees • Latency and jitter degradation for EDCF and HCF occur at essentially the same QoS loads - i.e. loads greater than the QoS load curve on Slide 19 • The contention-free period for HCF on Slide 19 is 90+% Aman Singla, Atheros

  21. Simulations – III (telephony case) Scenario • PHY = 36 Mb/s (11a), 10% PER • Background traffic 3 backlogged queues (ESTAs) @ 1500 Byte packets • QoS traffic 36 uni-directional (up-link) telephony streams Each stream = 120 Bytes @ 10ms CWMin/Max for EDCF = 7/15 Objective • Case study comparing EDCF and HCF Aman Singla, Atheros

  22. Latency Distribution (36 phones) EDCF delivers 4.71 Mb/s of background traffic x HCF uses 75% of channel time for CFP Delivers 4.77 Mb/s of background traffic Aman Singla, Atheros

  23. Conclusions – Simulations III • EDCF provides better latency and jitter in this case study • EDCF and HCF show similar efficiency (surplus bandwidth for background traffic) within the parameters of this case study • EDCF and HCF have similar QoS capacity for this experiment • HCF’s CFP duty cycle is 75% • Latency exceeds 10ms with more phones • Latency increase is caused by contention for EDCF, or scheduler conflicts for HCF Aman Singla, Atheros

  24. Analysis Aman Singla, Atheros

  25. Contention for low priority access AIFS Transmission Contention/arbitration for high priority access AIFS Operational behavior for EDCF • AIFS isolates contention between TCs • Traffic from one TC contends mostly against traffic within the same TC • AIFS projects an image of a lightly loaded network to higher priority applications by • Separating contention/arbitration for higher priority traffic from contention/arbitration for lower priority traffic • Deferring contention/arbitration for lower priority traffic until after contention/arbitration for higher priority traffic Aman Singla, Atheros

  26. Contention as a function of the number of stations Operational behavior for EDCF (cont.) • Admission Control is used to control the QoS load • Controlled load _ controlled contention Contention at excessive loads Contention at low loads Aman Singla, Atheros

  27. Operational behavior for HCF polling • Performance depends on the scheduler • Contention free channel access • Contention exists instead in the scheduler • Scheduled Polling • Phase difference between poll generation and data arrival contributes to latency • Error control • Channel error recovery contributes to scheduler complexity and overhead • Admission control is needed • Controlled load _ feasible schedule Aman Singla, Atheros

  28. Factors affecting latency • Packet size, PHY rate, etc. • Channel arbitration or scheduling artifacts • Background traffic • Channel packet errors Per-packet latency Aman Singla, Atheros

  29. TX fifo CBR source Wireless channel RX fifo CBR sink Summary (I) • Latency and jitter are a reality of the wireless network • Access method is just one contributing factor • Controlled access does not guarantee fixed or low latency • Similar latency and jitter for EDCF and HCF • Service guarantees will be probabilistic • Service guarantees will need Tx and Rx buffers • Simulation studies help determine buffer sizes • Similar buffer requirements for EDCF and HCF Aman Singla, Atheros

  30. Summary (II) • Service guarantees can be provided only on some limited QoS load • Admission Control is required for both EDCF and HCF • EDCF and HCF both work well within the limits • EDCF and HCF both fail when QoS load exceeds the limit • The QoS load limits for both EDCF and HCF are substantially the same • ~90% of maximal throughput may be used for QoS load • TCP congestion control arrives at the same operating point • Remaining channel may be used for background (best-effort) traffic with equal efficiency for both EDCF and HCF Aman Singla, Atheros

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