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 State 2 transition enabling condition state reset [Shaft00]

12 Cwnd of TCP Slow Start Fast Recovery Congestion Avoidance

13 Cwnd of TCP Slow Start Fast Recovery 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 When  i r i exceeds B the queue fills and data is lost (drops) rate = B bps drop (discrete event) r 1 bps r 2 bps r 3 bps q( t ) = queue size queue (temporary storage for data) f1 f1 f2 f2 f3 f3 f1 f1 f2 f2 f3 f3

17 Window-based rate adjustment w f (window size) = number of packets that can remain unacknowledged for by the destination 1 st packet sent e.g., w f = 3 t 2 nd packet sent 3 rd packet sent 1 st packet received & ack. sent 2 nd packet received & ack. sent 3 rd packet received & ack. sent 1 st ack. received ) 4 th packet can be sent t source fdestination f w f effectively determines the sending rate r f : t0t0 t1t1 t2t2 t3t3 00 11 22 propagation delay time in queue until transmission round-trip time

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

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

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 f1 f2 f2 f1 f1 f2 f2 F := { f 1, f 2, … } : set of end2end flows N := { n 1, n 2, … } : set of nodes L := { 1, 2, … } : set of links n1 n1 n2 n2 n3 n3 n4 n4 n5 n5 n6 n B = bandwidth of link T = prop. delay of link

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

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

24 Hybrid Queue Model -queue-not-full -queue-full transition enabling condition exported discrete event discrete modes

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

26 TCP: Slow Start 3.Until a drop occurs (or a threshold ssth f is reached), double w f on each RTT 4.When a drop occurs, divide w f and the threshold ssth f by 2 cong.-avoid. slow-start

27 TCP: Timeout, Fast Recovery 6.During fast recovery, data is sent at a rate consistent with a window size of w f /2 7.Duration of fast recovery (RTT) for Tcp-sack 5. Timeout occurs when

28 Full TCP: Sack

29 Congestion Control routing queue dynamics sending rates drops out-queue rates in-queue rates TCP RTTs

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 SimulatorSHIFTDYMOLA LanguageSHIFTMODELICA InstitutionBerkeleyDynasim SolverFixedFixed/Variable Analysis ToolNoYes Object OrientedYes SpeedSlowFast PlatformLinux/win32Redhat/win32 PublicYesNo 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 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 Y-topology dumbbell

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 modelns-2 the hybrid model accurately captures flow synchronization

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

36 Average throughput and RTT Thru. 1Thru. 2Thru. 3Thru. 4RTT1RTT2RTT3RTT4 ns hybrid model relative error2.6%7.9%1.5%.7%9.3%5.9%3.6%2.1% four competing TCP flow 5Mbps bottleneck throughput 20 trials with 10 minutes simulation time 10% UDP background traffic (exponentially distributed on-off times) the hybrid model accurately captures TCP unfairness in 10% relative error for different propagation delays 45,90,135,180ms propagation delays drop-tail queuing 5Mbps bottleneck throughput 10% UDP on/off background traffic

37 Empirical Distribution hybrid modelns-2 the hybrid model captures the whole distribution of congestion windows and queue size L-1 difference cwnd 1 cwnd 2 cwnd 3 cwnd 4 bottleneck queue dumbbell.71%.67%.71%.66%1.1% Y-shape.34%.44%.25%.33%.54%

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

40 Execution Time-2 dumbbell topology with 100ms propagation delay Execution time for 10 minOf simulation time [sec] 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 seconds of simulation time 4 TCP and 10 UDP flows with Y-Shape topology Execution time for 200 secOf simulation time [sec] 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 1.Tools to generate simulation code from a given topology 2.Improve scalability of simulator by extending hybrid technique (e.g. prediction of drop, aggregation of flows, skip multiple drop transition, removing fast recovery) 3.Extension to other forms of congestion control, queuing policies, and drop models (e.g. priority queuing, TCP-vegas, wireless, HTTP) 4.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 State 2 transition enabling condition state reset

50 Cwnd of TCP Slow Start Fast Recovery Congestion Avoidance

51 Cwnd of TCP Slow Start Fast Recovery Congestion Avoidance

52 Queue Size Queue Empty Queue Full Queue Not Full

53 Queue Size Queue Empty Queue Not Full Queue Full

54 Hybrid system model of TCP queue-not-full queue-full (drop occurs) (drop detected) transition enabling condition state reset

55 Drop probability vs. fraction of arrival rate Blue flow gets most of the drops, in spite of using a smaller fraction of bandwidth when synchronization occurs, with sufficient randomness drop probability

56 Dumbbell topology Several flows follow the same path and compete for bandwidth in a single bottleneck link Prototypical network to study congestion control single queuerouting is trivial q( t ) ´ queue size r 1 bps r 2 bps r 3 bps rate · B bps queue f1 f1 f2 f2 f3 f3 f1 f1 f2 f2 f3 f3

57 TCP Sack congestion control (Slow-Start) 1. While there are no drops, increase w i exponentially, being multiplied by 2 on each RTT 2. For an appropriately define constant m. If was constant we get 3. Since w f packets are sent each round-trip time, sending rate is

58 Slow Start : Dumbbell hybrid modelns-2 single TCP flow 5Mbps bottleneck throughput no background traffic

59 Execution Time-2 Execution time for 10 minutes of simulation time dumbbell topology with 20ms propagation delay Execution time for 10 minOf simulation time [sec]

60 Execution Time-4 Execution time for 200 seconds of simulation time 4 TCP and 10 UDP flows with Y-Shape topology Execution time for 200 secOf simulation time [sec] The hybrid model is faster than ns-2 when topology is more general such as Y-shape

61 Hybrid Queue Model (RED) Random Early Drop active queuing stochastic counter -queue-not-full -queue-full discrete modes

62 Window-based rate adjustment w i (window size) = number of packets that can remain unacknowledged for by the destination sending rate total round-trip time propagation delay per-packet transmission time time in queue until transmission queue gets full longer RTT rate decreases queue gets empty negative feedback

63 Related Work: Others Steady state (Sally96, Padhye99, Yang00, Bansal00) Dynamic (Low02, Paganini03) Stochastic (Ott96, Padhye99-tr, Bohacek03) Flowsim (Ahn96) Flow level (Hong03)

64 Related Work-1 (Packet Models) NS-2 (NS00) –Most widely used simulator –TCP, routing, multicast protocols over wired and wireless Pdns (Riley99) –Parallel/Distributed version of NS-2 QualNet –Evolved from GloMosim (Zeng98) and PARSEC (Bagrodia98) –Efficient and scalable simulation of wireless network SSFNET –Collection of Java based components for modeling and simulation of Internet protocols Opnet (Desbrandes93) –Originally developed at MIT and first commercial network simulator at 1987

65 Execution Time-3 Execution time for 10 minutes of simulation time dumbbell topology with 100ms propagation delay Execution time for 10 minOf simulation time [sec] The hybrid model is faster than ns-2 when bandwidth 1Gbps and there is 30 flows

66 On-Off CBR Model This is example of on-off CBR model and but on off period can follow any distribution

67 Related Work-2 (Analytical models) TCP model (Sally Floyd97, Mathis97) TCP friendly equation (Padhye98) -TCP ’ s steady state Throughput as a function of loss rate and RTT General and Binomial AIMD (Yang00, Bansal00) –Adjust sending rate by changing additive and multiplicative constant Equation Based Congestion Control (Padhye00) –TCP Friendly Rate Control (TFRC) protocol –Based on Padhye ’ s equation Dynamics of TCP/RED and scalable control (Low02) –TCP/RED becomes unstable when delay increases

68 Related Work-3 (Fluid Models) ATM model (Kesidis96) -Simulation speed up Time driven model (Yan99) Stochastic Differential Equation (Misra99, Misra00) –Sources receive Poisson loss rate Time Stepped hybrid simulation (Guo00) Comparison with packet model (Liu01) –Ripple effect More efficient and larger scale (Liu03) –Solving previous model numerically

69 Related Work-4 (Hybrid Models) Hybrid model for computer systems (Schwetman78) –Discrete event + analytical technique Adding fluid model to QualNet (Tak01) –Misra’s fluid model –Design an interface between a packet & fluid simulator Hybrid Discrete-Continuous Flow Network Simulator (Riley02) –Flows arrive as a messages with workload, priority, and itinerary Integrate packet and fluid model (Riley02) –Fluid modeling for background traffic: HDCF-NS (Melamed01) –Packet modeling for foreground traffic: PDNS (Riley99) Hybrid Packet/Fluid model (cameron03) –Fluid, packet, and hybrid mode buffer

70 Related Work: Analytical 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)

71 Related work-5 (others) Abstraction technique (Huang98) –Centralized computation, End-to-End, Packet Delivery, Algorithmic Routing, FSA Modeling Packet train (Ahn96) –Coarsening the network traffic

72 Analytical modelSimulations Steady state DynamicFluidSthochasticHybridFluidHybridPacket Sally96, Padhye9 9,Yang00, Bansal00 Misra99, Low02, paganini 03 Misra00, Liu03 Bohacek03Nicol98, Guo00, Kesidis96, Ahn96 Gu00, Schwetma n78, Tak01, Melamed0 1, Riley02, cameron03,huang99 NS-2, PDNS,Qua lNet, SSFNET, OPNET