1. 1 Novel Function Placement of Congestion Control Building Blocks in the Internet Kartikeya Chandrayana

2. 2 Outline Review Randomized TCP Uncooperative Congestion Control virtual AQM Conclusions

3. 3 Congestion Control Internet Meltdown –Need for congestion control. Congestion Avoidance and Control –End system based techniques. TCP –Network based solutions Active Queue Management (AQM) e.g. RED

4. 4 TCP Transmission Control Protocol –Protocol used to transport data –Source: Send a packet, Receiver: Acknowledge the packet Almost all applications (90%) use TCP What rate to send ? –No way of knowing what is the available bandwidth Probe for bandwidth –In some time “T” send w packets –If Acks for all w packets are rcvd then Send w+1 packets next time –Else Send w/2 packets

5. 5 End-System Based Solution TCP + Drop-Tail Queuing. TCP’s performance suffers on Drop-Tail queues. –Synchronization Congestion window of different flows increase and decrease simultaneously –Burst losses –Bias against flows with large RTT –Full Queues –Phase Effects Only a section of flows get dropped all the time –Lockout Effect Few flows monopolize the buffer space

6. 6 Active Queue Management Proactively Manage Queues –Drop packet before queue overflows –Small queues Probabilistic Dropping –Introduces randomization in network Early Congestion Indication Protect TCP Flows –CBR flows, selfish flows e.g. RED (and variants), REM, AVQ, CHOKe

7. 7 Random Early Drop (RED) Min th Max th avg: average queue length (EWMA) if avg < Min th then queue packet if avg > Max th then drop packet else, probabilistically drop/accept packet. Head Accept Drop/Mark Probabilistically Accept

8. 8 AQM Continued Have parameters which require configuration –e.g. Threshold to probabilistically drop packets Configuration Parameters are generally a function of link capacity, number of flows etc. –Small operating region –RED can perform worse than Drop-Tail queues AQMs are not deployed on the Internet Internet Works with Drop-Tail Queues Problems with Drop-Tail Queues Persist

9. 9 Review: Possible Solutions Some Buffer Mgmt. Scheme Users End System Based Solution: Use same congestion control algorithm Network Routers Network Based Solution: Use AQM/Scheduler in the network Limitation How do we verify the trust ? Constrains the choice of congestion control algorithms AQM Placement Required at every router. Limitations May require exchange of control information between all AQMs/Schedulers in the network. Generally only provides Max-Min Fairness. Most Solutions do not work with a Drop Tail queue Network What are the alternate architectural responses ?

10. 10 Proposed Solution Users Network Core Routers Uncooperative Congestion Control Virtual AQM Edge Routers Any queue mgmt algorithm  Drop Tail/RED etc. Minimal Changes/upgrades in the network Big, Fast Routers, Millions of Flows, Giga Bytes of Data First place where network can verify trust Medium Sized Routers, Manageable number of flow/data Randomized TCP Emulate Many Beneficial Properties of RED Protect TCP Flows, Manage Queues De-couple congestion control tasks from their placement

11. 11 Outline Review Randomized TCP Uncooperative Congestion Control virtual AQM Conclusions

12. 12 Randomized TCP Randomize the packet sending times –  = (1+x) RTT/W –X : Uniform(-1, 1) Always observe packet conservation TCP Randomized TCP

13. 13 Benefits of Randomized TCP End-System solution for introducing randomization in the network Emulates many beneficial properties of RED –Breaks synchronization –Spreads losses over time Independent losses –Removes Phase Effects –Removes Bias against large RTT flows –Reduces burst losses Competes fairly with TCP Reno

14. 14 Randomized TCP: Bias against large RTT flows Type Throughput (pkts/sec) % Share of the bottleneck Loss (%) LongReno132.05281.2 ShortReno333.58720.3 LongReno215.20440.3 ShortRandom277.86560.3 LongRandom214.89470.5 ShortRandom242.03530.5 LongReno-RED216.05460.3 ShortReno-RED256.80540.3 Single Bottleneck, Ideal Share: Long (43%), Short(57%) 60 ms 80 ms 2 Mbps 8 Mbps

15. 15 Randomized TCP: Phase Effects 8 Mbps 5 ms 8 Mbps 5 ms 0.8 Mbps 100 ms Randomized TCP removes phase effects

16. 16 Randomized TCP competes fairly with TCP Reno Removal of Phase Effects, Bias against large RTT flows, synchronization –Other Single bottleneck setups –Multi-bottleneck setups Even one Randomized TCP flow improves performance Randomized TCP reduces burst losses Randomized TCP improves performance of other window based rate control schemes –Binomial Congestion Control Randomized TCP: More Results Randomized TCP can emulate many beneficial properties of RED We can decouple management of synchronization, phase effect, bias against large RTT flows, burst losses from AQM design

17. 17 Outline Review Randomized TCP Uncooperative Congestion Control virtual AQM Conclusions

18. 18 New Congestion Control Schemes Application needs have changed –TCP not suitable –Different congestion control protocols Real-Player, Windows Media, Quake, Half-Life etc. Linux, FreeBSD Boxes came along –Make your own TCP. –If receive w acks then put w+5 packets in next RTT TCP send w+1 packets in next RTT –If congestion put 3w/4 packets in next RTT TCP send w/2 packets in next RTT

19. 19 Classification Responsive –React to congestion indication by cutting down its rate –e.g. TCP (and its variants) –Selfish/Mis-Behaving Maybe Un-responsive –Do not react to congestion indications –e.g. UDP, CBR –Selfish/Mis-Behaving Always

20. 20 Responsive vs Un-responsive 1 Mbps UDP Source Sending at 600Kbps Bandwidth left For TCP 1 Mbps Responsive Selfish Source Consistently looks at increasing it’s share

21. 21 Selfish Responsive Flows: Impact Drop Tail Queue 20 ms 0.8 Mbps 8 Mbps 5 ms TCP Flows shut out Traffic Volume Based Denial-of-Service Attack

22. 22 Possible Solutions Everyone uses TCP TCP Friendliness –Any rate control scheme gets the same throughput as TCP under same operating conditions. –x  1/sqrt(p) (x: rate, p : packet loss probability) Network Based Solutions –Use Active Queue Management (AQM) e.g. Random Early Drop (RED) –Min th, Max th, p, Q avg FRED, CHOKe etc. –Require Deployment at ALL routers

23. 23 AQM: Effect of Misbehavior RED RED Queue 20 ms 0.8 Mbps 8 Mbps 5 ms RED Helps: Though unfair sharing persists

24. 24 Other TCP Like Schemes TCP - Every RTT W(t+1) = W(t) +  (  = 1) if no loss W(t+1) = (1-  )W(t)(  = 0.5)otherwise Time-Invariant Schemes –Control parameters do not change with time Utility function does not change with time –Increase :  /f(W) f(W) > 0 –Decrease: (1-  )*g(W) 0 < g(W) < 1 –TCP Friendly Schemes f(W)g(W) = W –Binomial Congestion Control Schemes Increase:  /W k (t), Decrease: (1-  )W l (t) TCP Friendly Schemes given by k+l = 1

25. 25 Other TCP Like Schemes Time Invariant Schemes –Aggressive Selfish schemes:  > 1  > 0.5 f(W)g(W) < W e.g Increase: , Decrease:  W 0.5 (t) Time Variant Schemes –Control Parameters change with time –  (t) > 0 –  (t) > 0 –Increase: 1/W k (t), Decrease: W l (t) k(t) + l(t) = 0

26. 26 Consequences.. Users can choose their rate control scheme  Rate Control Scheme  rate allocation.  Aggressive Rate Control  More Rate  Incentive for users to misbehave.  But majority of users are responsible.  Traffic-Volume based denial-of-service attack Assume (for now) the network’s standard CC scheme is TCP Any scheme which gets more rate than TCP is uncooperative

27. 27 Detour: Congestion Control- Optimization Frameworks Utility Functions –Economics –One function can capture a group of rate control schemes. –TCP-Friendly schemes imply U(x)  -1/x x (Rate) U(x) 1020 1M 1M + 10

28. 28 Detour: Congestion Control- Optimization Frameworks Users choose congestion control algorithm  Choose a Utility Function  TCP : U(x)  -1/x  CC Scheme  Utility function Every user maximizes his own utility function Distributed optimization. Network imposes capacity constraints Total input rate cannot exceed capacity Communicates to users the price of using link Price : loss rate, mark (ECN), delay Users use this price to update their rate

29. 29 Optimization Framework: TCP TCP tries to minimize delay Equilibrium allocation (fairness) –Minimum Potential Delay Fairness Max-Min Fairness –U(x) = –1/x N (N  ) Proportional Fairness (TCP Vegas) –U(x) = log(x) Max -1/x s s.t. (  x s – C l )  0, for all l

30. 30 Work in the Utility Function Space Key Design Objectives: Deployment Ease Retain existing link price update rules.  No changes in the core. Retain existing user’s rate updation rules.  Allows users to chose rate control protocol. Should work with either drop or marking based network. Should work on a network of Drop Tail queues. U1U1 U2U2 UsUs Conformant Non Conformant U 1,U 2 define the conformance space U x ( Rate ) Selfish Map user’s Utility Function to Conformant Space x ( Rate ) U 1 = U 2 = -1/x UsUs Non Conformant U TCP Friendliness Map Map user’s Utility Function to Conformant Space

31. 31 User s is described by: –x s : Rate, U s : Utility function, q: end-to-end price –x s = U s ' -1 (q) –If source was using U obj then rate would be: x s = U obj ' -1 (q) Communicate to user the price q new : q new = U s ' (U obj ' -1 (q)) Now user’s update algorithm looks like x s = U s ' -1 (q new )  x s = U obj ' -1 (q)  Appears as if user is maximizing U obj ! Map user’s utility function to some (or range of) objective utility function U s  U obj, U obj  [U 1, U 2 ] How? By Penalty Function Transformation

32. 32 Core Network (No Changes) Any queue mgmt algorithm  Drop Tail/RED etc. Core Routers Edge Routers Edge Based Re-Marking Agent Maps utility function Manages Selfish Flows. ( Decouple it from AQM design ) Provides Service differentiation ( Map users to different utility functions ). Users Free to choose their congestion control algorithm Either marking or dropping Idea: Remap @Edge, Not in every Router Decouple Management of Selfish Flows from AQM Design

33. 33 What do we need to make it work ? Estimate utility function –Currently using Least Squares, Recursive LS –Needs only estimates of sending and loss rates Estimate loss/mark rate –Currently using EWMA, WALI methods of TFRC Need to identify misbehaving flows. –Smart Sampling in Netflow, Sample & Hold etc

34. 34 Utility Function Estimation Increase:  /x k (t), Decrease  x l (t) Utility function (n = k+l) –U = -  /(R  n (xR) n ) –U  -1/x n –U’(x) = p –log(p) = log(nK) – (n+1)log(x) Use linear least squares to estimate n

35. 35 Results: Single Bottleneck Drop Tail RED/ ECN Enabled x Mbps 4x Mbps 20 ms 5 ms TCP Reno, U=-1/x Mis-Behaving (U=-1/x 0.5 )

36. 36 Results: Multi-Bottleneck (Drop Tail) Framework prevents volume based denial of service attack. Without Re-Mapping TCP Flows shut out With Re-Mapping Drop-Tail Queue 20 ms 0.8 Mbps 8 Mbps 5 ms TCP Reno, U=-1/x Selfish (U=-1/x 0.5 )

37. 37 Results: Multi-Bottleneck (RED) Framework improves fair sharing of network Without Re-MappingWith Re-Mapping RED Queue 20 ms 0.8 Mbps 8 Mbps 5 ms TCP Reno, U=-1/x Selfish (U=-1/x 0.5 )

38. 38 Results: Multi-Bottleneck in an ECN Enabled Network Ideal Case No Re-Mapping With Re-Mapping RED Queue 20 ms 0.8 Mbps 8 Mbps 5 ms TCP Reno, U=-1/x Selfish (U=-1/x 0.5 ) Congestion Response Conformance

39. 39 Utility Function Estimation Results x Mbps 4x Mbps 20 ms 5 ms TCP Reno, U=-1/x Mis-Behaving (U=-1/x 0.5 ) N = 0.6, (Ideal: N=0.5) N = 0.8, (Ideal: N=1.0) Can estimate the exponent with a very small sample set

40. 40 More Results Background Traffic –Web (http) Traffic –Single/Multi Bottleneck scenarios Cross Traffic –Reverse path congestion –Especially important with RED –Multi-Bottleneck scenarios Comparison with other AQM schemes Differentiated Services

41. 41 Outline Review Randomized TCP Uncooperative Congestion Control virtual AQM Conclusions

42. 42 virtual AQM: Definitions R1R1 R2R2 R3R3 R4R4 Stream F Stream G Stream H I1I1 E1E1 I 1 - R 1 - R 2 - R 3 - E 1 : Path

43. 43 virtual AQM: Definitions Path Capacity : Minimum Link Capacity on a Path –Send a pair of back-to-back packets through Priority Queues Path Demand : Demand on a Path Send a packet train through data queue  a +  c C eff = 8*S/  c aa D = C - C eff Cross-Traffic  C = 8*S/  S Bytes

44. 44 virtual AQM: Algorithm  : network utilization (  < 1) Calculate virtual path capacity as C v =  * path Capacity Idea : Match Demand to Virtual path capacity at the network edge For every path –For every packet Drain virtual buffer as (t n -t n-1 )* C v Increase count of virtual buffer If virtual buffer overflows Drop(Mark) packets  At Steady State total input rate is less than the network capacity => smaller steady state queue

45. 45 virtual AQM: Results Demand Estimation vAVQ Drop TailAVQvAVQ Average Q Size18.6913.6212.22 Throughput (Mbps) 1.61.51.6 Fairness0.0670.030.06 We can decouple management of bottleneck queue from AQM design

46. 46 virtual AQM: Results Drop Tail Queue 20 ms 0.8 Mbps 8 Mbps 5 ms Demand Estimation vAVQ Drop TailAVQvAVQvAVQ* Average Q Size (First Bottleneck) 18.0011.4615.4814.02 Average Q Size (Second Bottleneck) 17.9710.8615.7214.66

47. 47 Conclusions Network based congestion avoidance and control solutions are not deployed De-couple congestion control task from it’s placement –Deployable architectures –Can get many beneficial properties of network based solutions Randomized TCP –End-System based solution –Can reduce synchronization, phase effects, bias against large RTT flows, burst losses –Emulate many beneficial properties of RED (AQM).

48. 48 Conclusions Un-Cooperative Congestion Control –Edge Based Solution –De-couple management of selfish flows from AQM design –Edge-based transformation of price can handle misbehaving flows –No changes in the core –Works with packet drop or packet marking (ECN) –Independent of buffer management algorithm virtual AQM –Edge-based proposal for managing bottleneck queues –For any path using packet probes find capacity and demand –Mark (drop) packets to match demand to path capacity –Results depend on estimation, length of virtual buffer –Initial Conceptual Prototype Presented

49. 49 References Kartikeya Chandrayana, Sthanunathan Ramakrishnan, Biplab Sikdar and Shivkumar Kalyanaraman, “On Randomizing the Sending Times in TCP and other Window Based Algorithms”, Conditional Accept for Journal of Computer Networks Kartikeya Chandrayana and Shivkumar Kalyanaraman, “Uncooperative Congestion Control”, ACM SIGMETRICS 2004, Also under submission to IEEE Transactions on Networking. Kartikeya Chandrayana and Shivkumar Kalyanaraman, “On Impact of Non-Conformant Flows on a Network of DropTail Gateways”, IEEE GLOBECOM 2003 K. Chandrayana, Y. Xia, B. Sikdar and S. Kalyanaraman, “A Unified Approach to Network Design and Control with Non-Cooperative Users”, RPI Networks Lab Tech Reoprt, ECSE-NET-2002-1, March 2002

50. 50 Randomized TCP: Synchronization BandwidthTCP RenoRandomized TCP 3 Mbps0.42540.1721 4 Mbps0.31520.1604 5 Mbps0.67000.0799 x Mbps 4x Mbps 20 ms 5 ms Randomized TCP reduces/removes synchronization

51. 51 virtual AQM: Improvements Drop TailvAVQvAVQ* Average Q Size18.6912.2210.94 Throughput (Mbps) 1.6 1.5 Fairness0.0670.060.05

52. 52 Simple Differentiated Services Multi-Bottleneck Setup: All flows are TCP Flows Objective: Increase the share of long flow by 10% Differentiated Services: Map users to different utility functions Edge Based

53. 53 Users Network Routers Placement Destination Mark Packets Mark Acks Mark Acks Drop Packets

54. 54 Re-Marker Design Implemented it in Network Simulator Estimation of loss rate Estimation of throughput Get utility function estimate Compute the Re-Marking function Appropriately Mark/Drop packets. –Can also Mark Acks Different Algorithm for CBR flows.