Handout # 13: Active Queue Management; Cells and Packets

1. Handout # 13: Active Queue Management; Cells and Packets CSC 2203 – Packet Switch and Network Architectures Professor Yashar Ganjali Department of Computer Science University of Toronto yganjali@cs.toronto.edu http://www.cs.toronto.edu/~yganjali TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.:

2. Announcements • Final presentations • Nov. 28th in class • 15 minutes for each talk (including Q&A) • Final report: Dec. 7th University of Toronto – Fall 2012

3. Active Queue Management*: Motivation • So far we have focused on admissible traffic. • What if an output link is congested? • Majority of flows use TCP for congestion control. • What if someone does not? University of Toronto – Fall 2012 * Thanks to Rong Pan, Cisco Systems.

4. Part I: 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 University of Toronto – Fall 2012

5. Queue Management: Drops/MarksA Feedback Mechanism To Regulate End TCP Hosts • End hosts send TCP traffic. • Network elements, switches/routers, generate drops/marks based on their queue sizes. • Drops/marks are regulation messages to end hosts. • TCP sources respond to drops/marks by cutting down their windows, i.e. sending rate. University of Toronto – Fall 2012

6. Drop Tail Problems • Lock out • Full queue • Bias against bursty traffic • Global synchronization University of Toronto – Fall 2012

7. Tail Drop – Lock Out Max Queue Length University of Toronto – Fall 2012

8. Tail Drop – Full Queue • Only drop packets when queue is full. • Feedback to sources when it is too late • Long steady-state delay University of Toronto – Fall 2012

9. Bias Against Bursty Traffic • When a smooth flow reaches a full queue it loses few packets. • Bursty flow on the other hand loses many packets. Max Queue Length University of Toronto – Fall 2012

10. Tail Drop – Global Synchronization • Different flows see packet drops at (almost) the same time. • Flows react similarly at the same point of time. • Thus we get synchronization. Max Queue Length University of Toronto – Fall 2012

11. 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. University of Toronto – Fall 2012

12. Active Queue Management – Goals • Solve tail-drop problems • No lock-out behavior • No global synchronization • No bias against bursty flow • Provide better QoS at a router • Low steady-state delay • Lower packet dropping University of Toronto – Fall 2012

13. Random Early Detection (RED) Arriving packet 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 University of Toronto – Fall 2012

14. RED Dropping Curve 1 Drop Probability maxp 0 minth maxth Average Queue Size University of Toronto – Fall 2012

15. Effectiveness of RED • Packets are randomly dropped. • Each flow has the same probability of being discarded. • Question. Does it address lock-out and global synchronization? University of Toronto – Fall 2012

16. Effectiveness of RED • Drop packets probabilistically in anticipation of congestion. • Not when queue is full • Use qavg to decide packet dropping probability. • Allow instantaneous bursts. • Questions. Does it address full-queue and bias against bursty traffic? University of Toronto – Fall 2012

17. 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. University of Toronto – Fall 2012

18. udp udp tcp tcp tcp udp tcp The Internet Connectionless; Best-Effort Bad News – Unresponsive End Hosts University of Toronto – Fall 2012

19. 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 • FQ, CSFQ (core stateless), SFQ (stochastic) • Queue management algorithms • RED, FRED, SRED University of Toronto – Fall 2012

20. No isolation from non-adaptive flows Easy to implement RED Fair Queuing vs. RED Isolation from non-adaptive flows Hard/Expensive to implement FQ University of Toronto – Fall 2012

21. Enhancing AQM: Fairness FIFO • Provide isolation from unresponsive flows. • Be as simple as RED University of Toronto – Fall 2012

22. yes 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 From RED to CHOKe Arriving packet RED CHOKe AvgQsize > Minth? no yes Admit the new packet AvgQsize > Maxth? no yes end Drop the new packet Admit packet with a probability p yes end end University of Toronto – Fall 2012

23. Random Sampling from Queue UDP flow A randomly chosen packet more likely from the unresponsive flow Adversary can’t fool the system University of Toronto – Fall 2012

24. Comparison of Flow ID • Compare the flow ID with the incoming packet: • More accurate. • Reduces the chance of dropping packets from a TCP-friendly flows. University of Toronto – Fall 2012

25. Dropping Mechanism • Drop packets (both incoming and matching samples ) • More arrival -> More Drop • Give users a disincentive to send more University of Toronto – Fall 2012

26. 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) University of Toronto – Fall 2012

27. 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 University of Toronto – Fall 2012

28. 74.1% 23.0% 99.6% 32 TCP, 1 UDP (one sample) University of Toronto – Fall 2012

29. 32 TCP, 5 UDP (5 samples) University of Toronto – Fall 2012

30. 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 University of Toronto – Fall 2012

31. Summary • Traditional Queue Management • Drop Tail, Drop Front, Drop Random • Problems: lock-out, full queue, global synchronization, bias against bursty traffic • Active Queue Management • RED: can’t handle unresponsive flows • CHOKe: penalize unresponsive flows University of Toronto – Fall 2012

32. References • R. Pan, B. Prabhakar, K. Psounis, “CHOKe, a stateless active queue management scheme for approximating fair bandwidth allocation”, Proceedings of the IEEE INFOCOM, 2:942-951, Tel Aviv, Israel, March 2000. • Ao Tang, Jiantao Wang, Steven H. Low, “Understanding CHOKe: throughput and spatial characteristics”, IEEE/ACM Transactions on Networking, Vol. 12, No. 4, 2004. University of Toronto – Fall 2012

33. VOQ11 Output 1 Input 1 VOQ1N VOQN1 Output N Input N VOQNN Cell Switching vs. Packet Switching • Slotted time • Fixed size data units  cells • Input-queued switch • Virtual output queues to avoid HoL blocking Switching Fabric University of Toronto – Fall 2012

34. Motivation • Packets have different lengths • Splitter module • Combiner module (memory) • Packet delays more important than Cell delays Switch VOQ11 Combiner VOQ1N Splitter VOQN1 VOQNN Can we use packet-based scheduling? University of Toronto – Fall 2012

35. Part II: Outline • Cell-based algorithms review: • Stability concept • Maximum weight matching algorithm • Packet-based algorithms • PB-MWM and its stability • PB algorithms classification • Work conserving • Waiting • Waiting PB algorithms University of Toronto – Fall 2012

36. : Number of cells arrived to VOQij up to time n : Number of cells departed from VOQij up to time n : Number of cells queued at VOQij at time n (SLLN) almost surely VOQ11 Output 1 Input 1 VOQ1N VOQN1 Output N Input N VOQNN Notation – Arrival rate Switching Fabric University of Toronto – Fall 2012

37. Admissibility and Rate Stability • The arrival rate matrix is “admissible” iff • A switch under a matching algorithm is “stable” (rate stable) if, almost surely, University of Toronto – Fall 2012

38. MWM Algorithm • A matching • MWM: At each time slot, select the matching with maximum weight University of Toronto – Fall 2012

39. MWM Stability • MWM is stable under i.i.d. Bernoulli traffic. • MWM is stable for any admissible traffic. • Fluid model technique • How about packet based MWM? N. McKeown,V. Ananthram, and J. Walrand, “Achieving 100% throughput in an input-queued switch,” INFOCOM 1996, pp. 296-302. J. G. Dai and B. Prabhakar, “The throughput of data switches with or without speedup,” INFOCOM 2000, pp. 556-564. University of Toronto – Fall 2012

40. Part II: Outline • Cell-based algorithms review: • Stability concept • Maximum weight matching algorithm • Packet-based algorithms • PB-MWM and its stability • PB algorithms classification • Work conserving • Waiting • Waiting PB algorithms University of Toronto – Fall 2012

41. Packet-Based Switching • Once the scheduler starts transmitting the first cell of a packet, it continues until the whole packet is received at output port University of Toronto – Fall 2012

42. Packet-Based Switching • Once the scheduler starts transmitting the first cell of a packet, it continues until the whole packet is received at output port University of Toronto – Fall 2012

43. Packet-Based Switching • Once the scheduler starts transmitting the first cell of a packet, it continues until the whole packet is received at output port. University of Toronto – Fall 2012

44. Cell-based to Packet-based • Consider cell-based algorithm X • At each time slot: • Busy ports: middle of sending a packet • Free ports: i/o ports can be assigned freely • PB-X • Keep the assignments used by busy ports • Find a sub-matching for free ports using algorithm X. University of Toronto – Fall 2012

45. Stability of PB-MWM • PB-MWM is stable under any “regenerative admissible traffic”. • Traffic is called “regenerative” if on average it requires a finite time to reach the state where all ports are free if it keeps using any fixed matching. • Bernoulli i.i.d. is a regenerative traffic. M.A. Marsan, A. Bianco, P. Giaccone, E. Leonardi, and F. Nari, “Packet Scheduling in Input-Queued Cell-based switches,” INFOCOM 2001, pp. 1085-1094 University of Toronto – Fall 2012

46. Proof Outline • Matching m(n) is “k-imperfect” if • For PB-MWM: • Lemma: A scheduling algorithm is rate stable if the average value of its weight is larger than maximum weight matching minus a bounded constant. University of Toronto – Fall 2012

47. Question • CB-MWM is stable under any admissible traffic. • PB-MWM is stable under any admissible regenerative traffic. Is the regenerative condition necessary? University of Toronto – Fall 2012

48. Counter-example University of Toronto – Fall 2012

49. Counter-example University of Toronto – Fall 2012

50. Counter-example University of Toronto – Fall 2012