1. Improving Adaptability and Fairness in Internet Congestion Control May 30, 2001 Seungwan Ryu PhD Student of IE Department University at Buffalo

2. 2 I. Internet Congestion Control Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

3. 3 I. Internet Congestion Control What is Congestion ? Congestion Control and Avoidance Implicit vs. Explicit feedback TCP Congestion Control Active Queue management (AQM) Explicit Congestion Notification (ECN)

4. 4 What is congestion ?  The aggregate demand for bandwidth exceeds the available capacity of a link. What will be occur ?  Performance Degradation Multiple packet loss Low link utilization (low Throughput) High queueing delay Congestion collapse

5. 5 Congestion Control and Avoidance Two approaches for handling Congestion  Congestion Control (Reactive) Play after the network is overloaded  Congestion Avoidance (Proactive) Play before the network becomes overloaded

6. 6 Implicit vs. Explicit feedback Implicit feedback Congestion Control  Network drops packets when congestion occur  Source infer congestion implicitly time-out, duplicated ACKs, etc.  Example: end-to-end TCP congestion Control  Simple to implement but inaccurate implemented only at Transport layer (e.g., TCP)

7. 7 Implicit vs. Explicit feedback - 2 Explicit feedback Congestion Control Network component (e.g., router) Provides congestion indication explicitly to sources use packet marking, or RM cells (in ATM ABR control) Examples: DECbit, ECN, ATM ABR CC, etc. Provide more accurate information to sources But is more complicate to implement Need to change both source and network algorithm Need cooperation between sources and network component

8. 8 TCP Congestion Control Use end-to-end congestion control  use implicit feedback e.g., time-out, triple duplicated ACKs, etc.  use window based flow control cwnd = min (pipe size, rwnd) self-clocking slow-start and congestion avoidance  Examples: TCP Tahoe, TCP Reno, TCP Vegas, etc.

9. 9 cwnd W W+1 RTT TCP Congestion Control - 2 Slow-start and Congestion Avoidance 1 2 4 RTT Slow Start Congestion Avoidance Time

10. 10 Active Queue Management (AQM) - 1 Performance Degradation in current TCP Congestion Control  Multiple packet loss  Low link utilization  Congestion collapse The role of the router (i.e., network)  Control congestion effectively with a network  Allocate bandwidth fairly

11. 11 AQM - 2 Problems with current router algorithm  Use FIFO based tail-drop (TD) queue management  Two drawbacks with TD: lock-out, full-queue Possible solution: AQM  Drop packets before buffer becomes full  Examples: RED, BLUE, ARED, SRED, FRED,….  Use (exponentially weighted) average queue length as an congestion indicator

12. 12 AQM - 3 Random Early Detection (RED)  use network algorithm to detect incipient congestion  Design goals: minimize packet loss and queueing delay avoid global synchronization maintain high link utilization removing bias against bursty source  Achieve goals by randomized packet drop queue length averaging

13. 13 P RED 1 max p min th max th K

14. 14 Active Queue Management (AQM) - 4 Problems with existing AQM Proposals  Mismatch between macroscopic and microscopic behavior of queue length  Insensitivity to the change of input traffic load  Configuration (parameter setting) problem Reasons:  Queue length averaging  use inappropriate congestion indicator  Use inappropriate control function

15. 15 Explicit Congestion Notification (ECN) Current congestion indication  Use packet drop to indicate congestion  source infer congestion implicitly ECN  to give less packet drop and better performance  use packet marking rather than drop  need cooperation between sources and network  need two bits in IP header: ECT-bit, CE-bit

16. 16 ECTCE CWR ECN - 2 4 3 2 1 TCP Header ECTCE 1 0 IP Header CWR 0 1 1 0 ACK TCP Header ECN-Echo 1 TCP Header CWR 1 SourceRouterDestination

17. 17 Contents Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

18. 18 II. Mathematical Modeling and Analysis An Overview Mathematical Modeling of AQM  Window based packet switching and the Internet  Mathematical modeling and analysis of AQM Problems with existing AQMs  Problems with existing AQMs  Adaptive congestion indicator and control function

19. 19 Overview Goal of mathematical modeling  see system dynamics (in steady state)  capture main factors influence to performance  provide design and/or operational recommendations Two approaches  Modeling steady state TCP behaviors the square root law, PFTK assume TD queue management at the router  Mathematical modeling and analysis of AQM (RED)

20. 20 Overview - 2 AQM modeling and analysis  Analytic modeling and analysis  Control Theoretic Analysis  Window based modeling and Analysis Assumptions  Poisson assumption for input traffic  Fixed number of persistent TCP traffics  Steady state window size saturation

21. 21 Mathematical Modeling of AQM Window based packet switching Model (Yang 99)  If link j is not congested  If link j is congested

22. 22 Mathematical Modeling of AQM - 2 Window size of an individual connection  Since Limitation of this model  Assume infinite buffer size No buffer overflow No packet drop No queue management algorithm at routers

23. 23 Mathematical Modeling of AQM - 3 s1s1 S2S2 S AQM Router Destination Sources Bottleneck Link 1 C 2 S Min_ th K A simple AQM model

24. 24 Mathematical Modeling of AQM - 4 Extend Yang’s Model to AQM model  Finite buffer capacity K  The router use AQM to control congestion  When congested Our Model: Yang’s Model:

25. 25 Mathematical Modeling of AQM - 5 Case 1: Tail drop  We obtain two relationship  Finally, packet drop probability P d :

26. 26 Mathematical Modeling of AQM - 6 Case 2: AQM  Let  Then  Packet drop prob. P d :

27. 27 Mathematical Modeling of AQM - 7 Congestion Indicator  Input traffic load should be the congestion Indicator  Current AQMs Use queue length Q as an alternative Assume that the input traffic load is fixed in equilibrium  Reason can not measure(or estimate) exactly for on line implementation of packet drop function

28. 28 Mathematical Modeling of AQM - 8 Packet drop function  Reason The traffic load fluctuate, NOT stay in equilibrium queue length is a function of input traffic  Alternatively:

29. 29 Problems with existing AQMs Mismatch between macroscopic and microscopic behavior of queue length Insensitivity to the input traffic load variation parameter configuration problem

30. 30 Problems with existing AQMs - 2 Mismatch problem

31. 31 Problems with existing AQMs - 3 Mismatch between macroscopic and microscopic behavior of queue length

32. 32 Problems with existing AQMs - 4 Insensitivity to the input traffic load variation  With light traffic (i.e., )

33. 33 Problems with existing AQMs - 5 Insensitivity to the input traffic load variation  With medium traffic (i.e., )

34. 34 Problems with existing AQMs - 6 Insensitivity to the input traffic load variation  With heavy traffic (i.e., )

35. 35 Problems with existing AQMs - 7 Parameter configuration problem  Has been a main design issue since 1993  many modified AQMs has been proposed Verified with simple simulation or simple experiment good for particular traffic conditions Real traffic is totally different.  Need adaptive congestion indicator and control function Adaptive to input traffic load variation Avoid congestion NOT based on current state (i,e,. Q)

36. 36 Contents Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

37. 37 III. Adaptive AQM and User Response Input traffic load Prediction Adaptive AQM algorithms Adaptive parameter configuration Adaptive User response algorithm

38. 38 Input traffic load Prediction Consider time-slotted model  Time is divided into unit time slots,  t, t=0,1,…  calculate parameters at the end of each slot  estimate Q t+1 to detect congestion proactively Predict from measured input traffic t-1, t of past two time slots Then, predict of next time slot  t

39. 39 Adaptive AQM algorithms Algorithm I: E-RED and E-GRED  Enhanced-RED  E-GRED: similar to E-RED

40. 40 Adaptive AQM algorithms - 2 Algorithm II:  Use both predicted traffic intensity and current buffer utilization  t =Q t /K  Possible algorithms:  Example: If  t is low and is high: more penalty to incoming packets If  t is high and is low: more penalty on existing packets Only High penalty for both packets when  t and are high

41. 41 Adaptive AQM algorithms - 3 Algorithm III: E-BLUE  BLUE Algorithm uses packet drops and link idle for adjusting packet drop probability Can not avoid some degree of performance degradation  Enhancement Use Virtual lower/upper bound (VL, VU) Combine predicted queue length with BLUE Impose penalty according to the traffic situation (, )

42. 42 Adaptive AQM algorithms - 4 E-BLUE  If, then p d = p d -   Else if VL < <VU, Else ( >VU) p d =p d + 

43. 43 Adaptive parameter configuration Adaptive queue length sampling interval  t  Previous recommendations In [22], minimum RTT was recommended In [65], static and link speed independent value was recommended However, models of [22, 65] were assumed to have persistent fixed N TCP traffics  Our recommendation The amount of incoming traffic fluctuate with time Adjust  t according to the varying traffic situation (i.e., adjust  t according to the amount of input traffic)

44. 44 Adaptive parameter configuration - 2  (i+2)  (i+1) ii  (i-1) Time Q

45. 45 Adaptive parameter configuration - 3 Adaptive filtering weight w q  In RED, w q was recommended with 0.02 for long-term (macroscopic) performance goal  Fixed small value of w q shows problems Parameter setting problem Insensitivity of control function to the change of traffic Fairness problem: impose penalty to innocent packets  Need to have adaptive w q to the change of traffic load  One possible method: Set w q as a function of current queue utilization, e.g., w q =  Q t /C, 0 <  < 1

46. 46 Adaptive User response algorithm AQM need work with intelligent source response for better performance Enhanced-ECN  If receive ECN feedback in  (t-1) If No ECN feedback in  t If received ACK > 0 Else Else, Continue usual response to ECN feedback  Else, Continue TCP Congestion Avoidance

47. 47 Contents Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan

48. 48 IV. Future Study Plan Future Study plan: a schedule Mathematical Modeling and Analysis  Stability and Control Dynamics  Alternative Modeling  Control Theoretic Consideration Simulation plan  Traffics  Performance Metrics

49. 49 Future Study plan: a schedule Documentation: Mathematical Modeling and Analysis Simulation plan Performance Metrics

50. 50 ( *,p*) p Mathematical Modeling and Analysis Since p=f(,q), Then find equilibrium point ( *,p * ) P=f( ) =g(p)

51. 51 Mathematical Modeling and Analysis - 2 Alternative Modeling:  State dependent service M/M/1 queueing model L=min th, K’=K-min th (  C+p K’-1 ) CC CC(  C+p 1 ) CC      10 LL-1 L-1 KK-1  C+

52. 52 Mathematical Modeling and Analysis - 3 Service rates Steady state probabilities

53. 53 Mathematical Modeling and Analysis - 3 Control Theoretic Consideration ACK (or NACK) t (1-p) t Control Function Queue dynamics Router Buffer SD

54. 54 Simulation plan Goal of simulation study  See dynamics and performance of our AQM  Compare results with other AQM such as RED Use realistic traffic  previous studies has been done with simple and unreal traffic (fixed number of persistent TCPs)  Generate realistic Internet traffic Long-lived (FTP) and short-lived (web-like) TCP traffic UDP traffic: CBR and/or ON/OFF

55. 55 Performance Metrics TCP traffics  Network-centric: for aggregate traffic Throughput (or goodput) Packet dropping (marking) probability Link utilization (or queueing delay)  User-centric: for Individual traffic goodput (or throughput) mean response time (RTT) UDP traffic individual packet drop probability and its distribution