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Improving Adaptability and Fairness in Internet Congestion Control

Improving Adaptability and Fairness in Internet Congestion Control. May 30, 2001 Seungwan Ryu PhD Student of IE Department University at Buffalo. I. Internet Congestion Control. Internet Congestion Control Mathematical Modeling and Analysis Adaptive AQM and User Response Future Study Plan.

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Improving Adaptability and Fairness in Internet Congestion Control

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

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

  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. What is congestion ? • 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. 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. 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. 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. 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. TCP Congestion Control - 2 • Slow-start and Congestion Avoidance cwnd Congestion Avoidance Slow Start W+1 W 4 2 1 RTT RTT Time

  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. 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. 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. RED P 1 maxp minth maxth K

  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. 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. ECN - 2 ECT CE ECT CE 1 0 1 1 IP Header 1 TCP Header 0 0 2 CWR CWR 1 ACK TCP Header ECN-Echo 3 TCP Header 1 CWR 4 Source Router Destination

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

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

  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. A simple AQM model Sources s1 1 AQM Router Destination Bottleneck Link S2 2 C S K Min_th SS Mathematical Modeling of AQM - 3

  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. Mathematical Modeling of AQM - 5 • Case 1: Tail drop • We obtain two relationship • Finally, packet drop probability Pd:

  26. Mathematical Modeling of AQM - 6 • Case 2: AQM • Let • Then • Packet drop prob. Pd:

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

  30. Problems with existing AQMs - 2 • Mismatch problem

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

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

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

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

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

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

  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 Qt+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. Adaptive AQM algorithms • Algorithm I: E-RED and E-GRED • Enhanced-RED • E-GRED: similar to E-RED

  40. Adaptive AQM algorithms - 2 • Algorithm II: • Use both predicted traffic intensity and current buffer utilization t=Qt/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. 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. Adaptive AQM algorithms - 4 • E-BLUE • If , then pd = pd-  • Else if VL < <VU, • Else ( >VU) • pd=pd+

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

  45. Adaptive parameter configuration - 3 • Adaptive filtering weight wq • In RED, wq was recommended with 0.02 for long-term (macroscopic) performance goal • Fixed small value of wq 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 wq to the change of traffic load • One possible method: • Set wq as a function of current queue utilization, • e.g., wq =  Qt/C , 0 <  < 1

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

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

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

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