1. 15744 Course Project1 Evaluation of Queue Management Algorithms Ningning Hu, Liu Ren, Jichuan Chang 30 April 2001

2. 15744 Course Project2 Motivation Typical queuing discipline: DropTail Active queue management: –RED (93’), BLUE (99’), … –FRED (97’), SFB (99’), CHOKe (2000’) Problem: How to compare them? –Metrics: Utilization, Fairness and Complexity –Method: simulation under common settings

3. 15744 Course Project3 Algorithms  Drop Tail: drop when the queue is full  RED: queue length, min th, max th, avg  FRED: per-active-flow accounting  BLUE: loss rate, link utilization, freeze_time  SFB: identify flows using N * L bins  CHOKe: compare incoming packet with random selected packet in queue

4. 15744 Course Project4 Simulation Scenario 2Mbps 1Mbps, 40ms 2Mbps UDP TCP UDP TCP Topology: Dumb-bell Metrics: throughput and queue size sourcedestination router

5. 15744 Course Project5 Drop Tail RED BLUE FREDSFBCHOKe TCP 1.# TCP Flow : # UDP Flow = 10 : 1 2.UDP sending rate = 2Mbps 3.Buffer size = 150 packets

6. 15744 Course Project6 Performance Comparison Figure 1. UDP Thpt (Mbps) Figure 2. UDP Queue Size (packet)

7. 15744 Course Project7 Conclusion Algorithm Link Utilization Fairness Ease of Configuration REDGoodUnfairHard BLUEGoodUnfairHard FREDGoodFairEasy ( adaptive ) SFBGoodFairEasy (non- adaptive ) CHOKeGoodFairEasy ( adaptive ) Algorithm Buffer size requirement Per-flow State Information Computational Overhead REDLargeNoArrival BLUESmallNoFreeze-time FREDSmallYesArrival-departure SFBLargeNoFreeze-time CHOKeSmallNoArrival

8. 15744 Course Project8 FRED RED + Per-active-flow accounting –protection of fragile flow –penalizing non-responsive flow –Flow type differentiation Key parameter: min q –2 or 4 Weak point: –Compute avg q per packet

9. 15744 Course Project9 FRED performance

10. 15744 Course Project10 FRED characteristics Easy to configure, adaptive Buffer size requirement –Will degrade as DropTail with too many flows –“Two-packet-buffer” to keep fairness

11. 15744 Course Project11 CHOKe Random Same FlowID? CHOose and Keep for responsive flows CHOose and Kill for unresponsive flows >max th ? min th max th

12. 15744 Course Project12 CHOKe

13. 15744 Course Project13 Parameters in CHOKe

14. 15744 Course Project14 BLUE Main Point: Uses packet loss and link idle events instead of average queue length : Based on observation that RED is often forced into drop-tail mode Adapt to how bursty and persistent congestion is by looking at loss/idle events

15. 15744 Course Project15 BLUE Algorithm Pm is Blue’s packet dropping probability: Upon packet loss, if no update of p m in freeze_time then increase by d1 Upon link idle, if no update of p m in freeze_time then decrease p m by d2 d1 >> d2  More critical to react quickly to increase in load

16. 15744 Course Project16 BLUE Characteristics More stable queue size and much better behavior with small buffers Good property damaged by non-responsive flows.

17. 15744 Course Project17 Stochastic Fair Blue(SFB) Objective : Identify and penalize misbehaving flows Main Algorithm : Create L hashes with N bins each Each bin keeps track of separate marking rate (p m ) Rate is updated using standard technique(as BLUE) Flow uses minimum p m of all L bins it belongs to Non-misbehaving flows hopefully belong to at least one bin without a bad flow

18. 15744 Course Project18 SFB Characteristics Could effectively detect non-responsive flows and rate-limit them But not adaptive, the bandwidth share of non-responsive flows is controlled by a parameter(Boxtime) offered by the user. Boxtime is the time interval no packets from non-responsive flows can be enqueued.

19. 15744 Course Project19 SFB: Unfairness of Non-responsive Flows Our solution: Randomized Boxtime a little bit.

20. 15744 Course Project20 Rate-limit Non-responsive flows Time UDP packets Queue Boxtime