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Active Queue Management
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  1. Active Queue Management Rong Pan Cisco System EE384y Spring Quarter 2008

  2. Outline • Queue Management • Drop as a way to feedback to TCP sources • Part of a closed-loop • Traditional Queue Management • Drop Tail • Problems • Active Queue Management • RED • CHOKe • AFD

  3. Queue Management: Drops/Marks- A Feedback Mechanism To Regulate End TCP Hosts • End hosts send TCP traffic -> Queue size • Network elements, switches/routers, generate drops/marks based on their queue sizes • Drops/Marks: regulation messages to end hosts • TCP sources respond to drops/marks by cutting down their windows, i.e. sending rate

  4. TCP+Queue Management- A closed-loop control system C W/R _ + q  N  + _ 0.5 - p Time Delay Queue Management + 1

  5. Drop Tail- problems • Lock out • Full queue • Bias against bursty traffic • Global synchronization

  6. Tail Drop Queue ManagementLock-Out Max Queue Length

  7. Tail Drop Queue ManagementFull-Queue • Only drop packets when queue is full • long steady-state delay

  8. Bias Against Bursty Traffic Max Queue Length

  9. Tail Drop Queue ManagementGlobal Synchronization Max Queue Length

  10. Alternative Queue Management Schemes • Drop from front on full queue • Drop at random on full queue • both solve the lock-out problem • both have the full-queues problem

  11. Active Queue ManagementGoals • Solve tail-drop problems • no lock-out behavior • no full queue • no bias against bursty flow • no global synchronization • Provide better QoS at network nodes • low steady-state delay • lower packet dropping

  12. Random Early Detection (RED) Arrivingpacket AvgQsize > Minth? no yes Admit the new packet AvgQsize > Maxth? no yes end Drop the new packet Admit packet with a probability p end end

  13. RED Dropping Curve 1 Drop Probability maxp 0 minth maxth Average Queue Size

  14. Effectiveness of RED- Lock-Out & Global Synchronization • Packets are randomly dropped • Each flow has the same probability of being discarded

  15. Effectiveness of RED- Full-Queue & Bias against bursty traffic • Drop packets probabilistically in anticipation of congestion • not when queue is full • Use qavg to decide packet dropping probability: allow instantaneous bursts

  16. What QoS does RED Provide? • Lower buffer delay: good interactive service • qavg is controlled to be small • Given responsive flows: packet dropping is reduced • early congestion indication allows traffic to throttle back before congestion • Given responsive flows: fair bandwidth allocation

  17. udp udp tcp tcp tcp udp tcp The Internet Connectionless; Best-Effort Bad News - unresponsive end hosts

  18. Scheduling & Queue Management • What routers want to do? • isolate unresponsive flows (e.g. UDP) • provide Quality of Service to all users • Two ways to do it • scheduling algorithms: e.g. FQ, CSFQ, SFQ • queue management algorithms with fairness enhancement: e.g. CHOKe, AFD, WRED

  19. Active Queue Manament With Enhancement to Fairness FIFO • Approximate fair bandwidth allocation • Provide isolation from unresponsive flows • Be as simple as RED

  20. CHOKe no Draw a packet at random from queue Flow id same as the new packet id ? no yes AvgQsize > Maxth? Drop both matched packets no yes Drop the new packet Admit packet with a probability p end end end RED Arrivingpacket AvgQsize > Minth? no yes Admit the new packet AvgQsize > Maxth? no yes end Drop the new packet Admit packet with a probability p end end

  21. Random Sampling from Queue UDP flow • A randomly chosen packet more likely from the unresponsive flow • Adversary can’t fool the system

  22. Comparison of Flow ID • Compare the flow id with the incoming packet • more acurate • Reduce the chance of dropping packets from a TCP-friendly flows.

  23. Dropping Mechanism • Drop packets (both incoming and matching samples ) • More arrival -> More Drop • Give users a disincentive to send more

  24. Simulation Setup S(1) D(1) S(2) 10Mbps 10Mbps D(2) m TCP m TCP Sources Sinks 1Mbps S(m) D(m) S(m+1) D(m+1) n UDP Sources n UDP Sinks S(m+n) D(m+n)

  25. Network Setup Parameters • 32 TCP flows, 1 UDP flow • All TCP’s maximum window size = 300 • All links have a propagation delay of 1ms • FIFO buffer size = 300 packets • All packets sizes = 1 KByte • RED: (minth,maxth) = (100,200) packets

  26. 32 TCP, 1 UDP (one sample) 74.1% 23.0% 99.6%

  27. 32 TCP, 5 UDP (5 samples)

  28. Rk R2 R1 How Many Samples to Take? Maxth avg minth • Different samples for different Qlenavg • # samples  when Qlenavg close to minth • # samples when Qlenavg close to maxth

  29. 32 TCP, 5 UDP (self-adjusting)

  30. AnalyticalModel discards from the queue permeable tube with leakage

  31. Fluid Analysis • N: the total number of packets in the buffer • Li(t): the survival rate for flow i packets Li(t)t - Li(t +t)t = i tLi(t)t/N - dLi(t)/dt = i Li(t) N Li(0) = i (1-pi ) Li(D) = i (1-2pi )

  32. Model vs Simulation- multiple TCPs and one UDP 1/(1+e)

  33. Fluid Model - Multiple samples • Multiple samples are chosen Li(t)t - Li(t +t)t = Mi tLi(t)t/N - dLi(t)/dt = Mi Li(t) N Li(0) = i (1-pi )M Li(D) = i (1-pi )M - Mi pi

  34. Two Samples- multiple TCPs and one UDP

  35. Two Samples- multiple TCPs and two UDP

  36. What If We Use a Small Amount of State?

  37. AFD: Goal • Approximate weighted bandwidth allocation • Not only AQM, approximate WDRR scheduling • Provide soft queues in addition to physical queues • Keep the state requirement small • Be simple to implement

  38. Mfair = f(Qlen, Qlen_old,Qref) Fair Share Mi = Arrival estimate for Class i (Bytes over interval Ts) If Mi Mfair : No Drop (Di = 0) If Mi > Mfair : Di > 0 such that Mi (1-Di) = Mfair AFD Algorithm: Details (Basic Case: Equal Share) Di = Drop Probability for Class i Arriving Packets Qlen 1-Di Qref Class i Di

  39. Mfair = f(Qlen, Qlen_old,Qref) Fair Share Mi = Arrival estimate for Class i (Bytes over interval Ts) If Mi F(Mfair,Mini,Maxi,Wi, …): No Drop (Di = 0) If Mi > Mfair : Di > 0 such that Mi (1-Di) = F(Mfair,Mini,Maxi,Wi, …) AFD Algorithm: Details (General Case) Di = Drop Probability for Class i Arriving Packets Qlen 1-Di Qref Class i Di

  40. Not Per-Flow State Fraction of flows • State requirement on the order of # of unresponsive flows • Elephant Traps (developed jointly Stanford and Cisco)

  41. AFD Solution: Details • Based on 3 simple mechanisms • estimate per “class” arrival rate • counting per “class” bytes over fixed intervals ( Ts ) • potential averaging over multiple intervals • estimate deserved departure rate (so as to achieve the proper bandwidth allocation for each class) • observation of queue length as measure of congestion • perform selective dropping (pre-enqueue) to drive arrival rate to the desired departure rate

  42. Mixed Trafficwith Different Levels of Unresponsiveness

  43. Drop Probabilities(note differential dropping)

  44. Different Number of TCP Flows in Each Class 10 TCP Flows Class 2 Class 1 5 TCP Flows 0 50 100 150 200 time 0 50 100 150 200 time 20 TCP Flows 15 TCP Flows Class 4 Class 3 0 50 100 150 200 time 0 50 100 150 200 time

  45. Different Class Throughput Comparison

  46. Queue Length

  47. Mfair

  48. AFD Implementation Issues • Monitor Arrival Rate • Determine Drop Probability • Maximize Link Utilization

  49. Arrival Monitoring • Keep a counter for each class • Count the data arrivals (in bytes) of each class in 10ms interval: arvi • Arrival rate of each class is updated every 10ms • mi = mi(1-1/2c)+arvi • c determines the average window

  50. Implementing the Drop Function • If Mi Mfairthen Di = 0 • Otherwise, rewrite the drop function as • Suppose we have predetermined drop levels, find the one such that Di* Mi = (Mi – Mfair)