A Hybrid Systems Modeling Framework for Data Communication Networks

1. A Hybrid Systems Modeling Framework for Data Communication Networks Ph.D Dissertation Proposal Junsoo Lee 9/5/2003

2. Studying Networks… • Study of networks and network protocols have used: • Analytical models. • Simulation tools. • Limitations: • Analytical models • Significant accuracy loss • Only applicable to limited application • Simulation tools • Long simulation time • Large memory overhead

3. Motivation • Simulation speed up • Faster than packet level simulation • More accurate than fluid simulation • Validate designs through simulation • Scalability, performance • Analyze and design protocols • Throughput, fairness, security • Tune network parameters • Queue size, bandwidth

4. Expected contribution • Provide a scalable framework for the design, analysis, and evaluation of large-scale computer networks and their protocol • Contribute to the networking research and industry communities by allowing efficient and accurate simulation of large-scale network • Provide tools to generate hybrid model without programming by generating automatic simulation code from a given network topology. • Provide test environment of the network protocols on networks with large delay bandwidth product

5. Talk Outline • Related work • Simplified Hybrid Model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

6. Related Work: Packet model • Track individual data packets • Computationally intensive • Complexity depends on the number of events • Does not scale to large bandwidth and complex topology • NS-2 (NS00) • Pdns (Riley99) • QualNet • Opnet (Desbrandes93) • SSFNET

7. Related Work: Fluid Model • Track time/ensemble-average packet rates • Computationally efficient • Complexity depends on the rate changes • Only suitable to model many flows • Does not explicitly model individual event • ATM (Kesidis96) • Time driven (Yan99) • Stochastic Differential Equation (Misra99,20) • Time-Stepped Hybrid Simulation (Guo00) • Fluid-Simulation using SSF (Nicol98) • More efficient and larger scale (Liu03)

8. Related Work: Hybrid model • Discrete Event + analytical technique • Packet (foreground) + fluid model (back-ground) • Packet (edge) + fluid mode (backbone) • Abstract technique • Computer systems (Schwetman78) • Fluid model extension to QualNet (Tak01) • HDCF-NS (Melamed01) • HDCF-NS + PDNS (Riley02) • Hybrid mode buffer (cameron03) • Abstract technique (Huang99)

9. Our Approach: Hybrid model • Track packet rates for each flow averaged over small time scales • explicitly models some discrete events (drops, queues becoming empty, etc.) • time accuracy of a few milliseconds (round-trip time) • Key idea presented at SIGMETRIC 2003

10. Talk Outline • Related work • Simplified Hybrid Model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

11. Simple Hybrid Model Example State 1 transition enabling condition State 2 state reset [Shaft00]

12. Cwnd of TCP Slow Start Fast Recovery Congestion Avoidance

13. Cwnd of TCP Fast Recovery Slow Start Congestion Avoidance

14. Queue Size Queue Empty Queue Full Queue Not Full

15. Queue Size Queue Empty Queue Not Full Queue Full

16. Dumbbell topology f1 f1 queue (temporary storage for data) r1 bps f2 r2 bps f2 rate =B bps q(t) = queue size f3 r3 bps f3 When åi ri exceeds B the queue fills and data is lost (drops) drop(discrete event)

17. Window-based rate adjustment wf (window size) = number of packets that can remain unacknowledged for by the destination source f destination f e.g., wf = 3 t0 1st packet sent t1 2nd packet sent t2 t0 3rd packet sent 1st packet received & ack. sent t1 2nd packet received & ack. sent t2 t3 1st ack. received )4th packet can be sent 3rd packet received & ack. sent t t time in queueuntil transmission wf effectively determines the sending rate rf: round-trip time propagation delay

18. TCP Sack Congestion Control • While there are no drops, increase wfby 1 on each RTT • When a drop occurs, divide wiby 2 (congestion controller constantly probe the network for more bandwidth) TCP controllers Queuing model rf RTT drop Consider only CA for now for the simplicity

19. Hybrid system model for TCP transition enabling condition additive-increase (drop) state reset

20. Talk Outline • Related work • Simplified hybrid model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

21. General Topology f1 n4 n1 3 1 n3 n6 5 f2 f2 4 2 n5 n2 f1 N := { n1, n2, … } : set of nodes B = bandwidth of link  T = prop. delay of link  L := { 1, 2, … } : set of links F := { f1, f2, … } : set of end2end flows

22. Queue Dynamics  in-queue rates out-queue rates drop rates M M … Queue dynamics: queue size due to flowf total queue size the packets of each flow are assumed uniformly distributed in the queue

23. Queue Dynamics  in-queue rates out-queue rates drop rates M M … same in and out-queue rates queue empty no drops queue not empty/full queue full drops proportional to fraction in-queue rates out-queue rates proportional to fraction of packets in the queue

24. Hybrid Queue Model -queue-not-full transition enabling condition discrete modes exporteddiscrete event -queue-full

25. TCP: AIMD • While there are no drops, increase wf by 1 on each RTT (additive increase) • When a drop occurs, divide wf by 2 (multiplicative decrease) • (congestion controller constantly probe the network for more bandwidth) importeddiscrete event propagation delays congestion-avoidance set of links transversed by flow f

26. TCP: Slow Start • Until a drop occurs (or a threshold ssthf is reached), double wf on each RTT • When a drop occurs, divide wf and the threshold ssthf by 2 slow-start cong.-avoid.

27. TCP: Timeout, Fast Recovery 5. Timeout occurs when • During fast recovery, data is sent at a rate consistent with a window size of wf /2 • Duration of fast recovery (RTT) for Tcp-sack

28. Full TCP: Sack

29. Congestion Control routing in-queue rates RTTs sendingrates out-queuerates queue dynamics TCP drops

30. Talk Outline • Related work • Simplified Hybrid Model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

31. Comparison of Hybrid Model Simulation Environments Dymola has variety of solvers and efficient methods for determining when discrete events occur

32. Validation Methodology • Compared simulation results from • ns-2 packet-level simulator • hybrid models implemented in Modelica and Shift • Plots in the following slides refer to two test topologies Y-topology dumbbell • 10ms propagation delay • drop-tail queuing • 5-500Mbps bottleneck throughput • 45,90,135,180ms propagation delays • drop-tail queuing • 5-500Mbps bottleneck throughput • 0-10% UDP on/off background traffic

33. Slow Start : Dumbbell • single TCP flow • 5Mbps bottleneck throughput • no background traffic

34. 4 flow : Dumbbell • four competing TCP flow • 5Mbps bottleneck throughput • no background traffic hybrid model ns-2 the hybrid model accurately captures flow synchronization

35. 4 flows with BG:Y-shape • four competing TCP flow • 5Mbps bottleneck throughput • 10% UDP background traffic(exponentially distributedon-off times) hybrid model ns-2

36. Average throughput and RTT • four competing TCP flow • 5Mbps bottleneck throughput • 20 trials with 10 minutes simulation time • 10% UDP background traffic(exponentially distributedon-off times) • 45,90,135,180ms propagation delays • drop-tail queuing • 5Mbps bottleneck throughput • 10% UDP on/off background traffic the hybrid model accurately captures TCP unfairness in 10% relative error for different propagation delays

37. Empirical Distribution hybrid model ns-2 the hybrid model captures the whole distribution of congestion windows and queue size

38. Talk Outline • Related work • Simplified Hybrid Model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

39. Execution Time-1 1 flow 3 flows 500Mbps ns-2 50Mbps hybrid model 5Mbps number of flows • ns-2 complexity approximately scales with • hybrid simulator complexity approximately scales with (# packets) per-flow throughput hybrid models are particularly suitable for large, high-bandwidth simulations (satellite, fiber optics, backbone)

40. Execution Time-2 Execution time for 10 minOf simulation time [sec] • dumbbell topology with 100ms propagation delay The hybrid model is hundred times faster than ns-2 when bandwidth 1Gbps and there is 30 flows

41. Execution Time-3 Execution time for 200 secOf simulation time [sec] • Execution time for 200 seconds of simulation time • 4 TCP and 10 UDP flows with Y-Shape topology The hybrid model is 50 times faster than ns-2 with Y-shape topology

42. Talk Outline • Related work • Simplified Hybrid Model of TCP • Generalized Hybrid Model Framework • Validation • Simulation Complexity • Contributions, Proposed Work & Schedule • Conclusion

43. Contribution (so far) • Apply hybrid systems to model communication network for the first time • Develop hybrid framework for TCP congestion control and validate it by comparing to packet-level simulations • Implement network model using SHIFT and Modelica hybrid model language • Simulation speed up to few hundred times compare to packet model • Simple automatic hybrid model generator from network topology • Develop On-off TCP flows characterizes on period using some file size and off period using some distribution

44. Proposed work • Tools to generate simulation code from a given topology • Improve scalability of simulator by extending hybrid technique (e.g. prediction of drop, aggregation of flows, skip multiple drop transition, removing fast recovery) • Extension to other forms of congestion control, queuing policies, and drop models (e.g. priority queuing, TCP-vegas, wireless, HTTP) • Illustrate and verify protocol for high delay and bandwidth product (e.g. FAST TCP)

45. Expected contribution • Provide a scalable framework for the design, analysis, and evaluation of large-scale computer networks and their protocol • Contribute to the networking research and industry communities by allowing efficient and accurate simulation of large-scale network • Provide tools to generate hybrid model without programming by generating automatic simulation code from a given network topology. • Provide test environment of the network protocols on networks with large delay bandwidth product

46. Schedule • Fall 2003 • Develop tools to generate hybrid simulation code from a given topology • Fall 2003 – Winter 2003 • Improve scalability by extending hybrid technique • Spring 2004 • Extend to other forms of congestion control, queuing policies, and drop models • Study on network protocol for large delay bandwidth product • Summer 2004 • Dissertation writing • Ph. D Defense

47. Conclusion • Hybrid Systems provide a promising approach to model network traffic • Retain the low-dimensionality of continuous approximations to traffic flow • Represent event based control mechanisms with high accuracy, even at small time-scales • Complexity scales inversely with throughput and RTT • Amenable to formal analysis

48. Thank you!!!

49. Simple Hybrid Model Example State 1 transition enabling condition State 2 state reset

50. Cwnd of TCP Slow Start Fast Recovery Congestion Avoidance