a hybrid systems modeling framework for data communication networks ph.d dissertation proposal...

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



11

Simple Hybrid Model Example

State 1

State 2

Cx

),( yxfy

Dx

),( yxgy

?by

?ax

0:x

transition enabling condition

state reset

[Shaft00]

12

Cwnd of TCP

Slow Start

Fast Recovery

Congestion Avoidance

13

Cwnd of TCP

Slow StartFast

Recovery


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 ri exceeds B the queue fills and data is lost (drops)

rate = B bps

drop (discrete event)

r1 bps

r2 bps

r3 bpsq( t ) = queue size

queue (temporary storage for data)

f1

f2

f3

f1

f2

f3

17

Window-based rate adjustmentwf (window size) = number of packets that can remain

unacknowledged for by the destination

1st packet sent

e.g., wf = 3

t

2nd packet sent3rd packet sent 1st packet received & ack. sent

2nd packet received & ack. sent3rd packet received & ack. sent1st ack.

received )4th packet can be sent

t

source f destination f

wf effectively determines the sending rate rf :

t0

t1

t2

t3

0

1

2

propagation delay

time in queueuntil transmission

round-trip time

18

TCP Sack Congestion Control

B

qTRTT p

RTTw f

1

1. While there are no drops, increase wf by 1 on each RTT2. When a drop occurs, divide wi by 2

(congestion controller constantly probe the network for more bandwidth)

Queuing model TCP controllers

drop

RTT

rf

RTT

wr f

f

f

f Brqq max, 2

w

w f

Consider only CA for now for the simplicity

19

Hybrid system model for TCP

(drop)


state reset

additive-increase

20

Talk Outline

• Related work• Simplified hybrid model of TCP • Generalized Hybrid Model Framework• Validation • Simulation Complexity• Contributions, Proposed Work &


21

General Topology f1

f2

f1

f2

F := { f1, f2, … } : set of end2end flows

N := { n1, n2, … } : set of nodes

L := { 1, 2, … } : set of links

n1

n2

n3

n4

n5

n6

1

2

3

4

5

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


exporteddiscrete event

discrete modes

25

TCP: AIMD

congestion-avoidance

set of links transversed by flow f

propagation delays

1. While there are no drops, increase wf by 1 on each RTT (additive increase)

2. When a drop occurs, divide wf by 2 (multiplicative decrease)

(congestion controller constantly probe the network for more bandwidth)

importeddiscrete event

26

TCP: Slow Start

3. Until a drop occurs (or a threshold ssthf is reached), double wf on each RTT4. When a drop occurs, divide wf and the threshold ssthf by 2

cong.-avoid.slow-start

27

TCP: Timeout, Fast Recovery

dropf

f

dropf

ntw

tw

Floorntwn

2

)(1

2

)(1

log:)),((0

0

20

}42,2max{ dropdropf nnw

2/)( 0 twn fdrop

12/)( 0 twn fdrop dropdropf nFloorntwn 20 log1:)),((

6. During fast recovery, data is sent at a rate consistent with a window size of wf /2

7. Duration of fast recovery (RTT) for Tcp-sack

5. Timeout occurs when

28

Full TCP: Sack

29

Congestion Controlrouting

queue dynamics

sendingrates

drops

out-queuerates

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 &


31

Comparison of Hybrid Model Simulation Environments

Simulator SHIFT DYMOLA

Language SHIFT MODELICA

Institution Berkeley Dynasim

Solver Fixed Fixed/Variable

Analysis Tool No Yes

Object Oriented Yes Yes

Speed Slow Fast

Platform Linux/win32 Redhat/win32

Public Yes No

Dymola has variety of solvers and efficient methods for determining when discrete events occur

32

Validation MethodologyCompared 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-topologydumbbell

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

hybrid model

• four competing TCP flow• 5Mbps bottleneck throughput• 10% UDP background traffic

(exponentially distributedon-off times)

ns-2

36

Average throughput and RTT

Thru. 1 Thru. 2 Thru. 3 Thru. 4 RTT1 RTT2 RTT3 RTT4

ns-2 1.873 1.184 .836 .673 .0969 .141 .184 .227

hybrid model 1.824 1.091 .823 .669 .0879 .132 .180 .223

relative error 2.6% 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 distributedon-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 model ns-2

the hybrid model captures the whole distribution of congestion windows and queue size

L-1 difference

cwnd1

cwnd2

cwnd3

cwnd4

bottleneck queue

dumbbell .71% .67% .71% .66% 1.1%

Y-shape .34% .44% .25% .33% .54%

38

Talk Outline



39

Execution Time-1

0.1

1

10

100

1000

10000

1 10 100 1000

bottleneck bandwidth [Mbps]

execution tim

e for

10m

in

of sim

ula

tion tim

e

[sec]

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

Exe

cutio

n tim

e fo

r 10

min

Of s

imul

atio

n tim

e [s

ec]

The hybrid model is hundred times faster than ns-2 when bandwidth 1Gbps and there is 30 flows

sec

0.01

0.1

1

10

100

1000

10000

10 100 1000

Bottleneck Bandwidth [Mbps]

1 flow-hybrid

10 flows-hybrid

20 flows-hybrid

30 flows-hybrid

1 flow-ns

10 flows-ns

20 flows-ns

30 flows-ns

41

1

10

100

1000

10000

5 50 500


sec hybrid

ns

Execution Time-3

• Execution time for 200 seconds of simulation time

• 4 TCP and 10 UDP flows with Y-Shape topology

Exe

cutio

n tim

e fo

r 20

0 se

cO

f sim

ulat

ion

time

[sec

]

The hybrid model is 50 times faster than ns-2 with Y-shape topology

42

Talk Outline



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

Cx

),( yxfy

Dx

),( yxgy

by

?ax

0:x


state reset

50

Cwnd of TCP

Slow Start

Fast Recovery


51

Cwnd of TCP

Slow StartFast

Recovery


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)


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

r1 bps

r2 bps

r3 bps

rate · B bps

queue

f1

f2

f3

f1

f2

f3

57

TCP Sack congestion control (Slow-Start)

)()()(1

log

tmwtweRTTtW ff

dtRTT

m

ff

fRTTt

t f

f

ff RTT

wr

1. While there are no drops, increase wi exponentially, being multipliedby 2 on each RTT

ff

f wRTT

mw

log

2. For an appropriately define constant m. If was constantwe get

fRTT

3. Since wf packets are sent each round-trip time, sending rate is

58

Slow Start : Dumbbell

hybrid model ns-2

• single TCP flow• 5Mbps bottleneck throughput• no background traffic

59

Execution Time-2

• Execution time for 10 minutes of simulation time


0

500

1000

1500

2000

2500

3000

10 100 1000

Bottleneck Bandwidth [Mbps ]

sec

1 flow- hybrid10 flows - hybrid20 flows - hybrid30 flows - hybrid1 flow- ns10 flows - ns20 flows - ns30 flows - nsE

xecu

tion

time

for

10 m

inO

f sim

ulat

ion

time

[sec

]

60

Execution Time-4

0

200

400

600

800

1000

1200

1400

1600

5 50 500


sec hybrid

ns

• Execution time for 200 seconds of simulation time

• 4 TCP and 10 UDP flows with Y-Shape topology

Exe

cutio

n tim

e fo

r 20

0 se

cO

f sim

ulat

ion

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 Dropactive queuing

stochastic counter-queue-not-full

-queue-full

discrete modes

62

Window-based rate adjustment

i

ii RTT

twtr

)()(

wi (window size) = number of packets that can remain unacknowledged for by the destination sending rate

totalround-trip

time propagationdelay

per-packettransmission time

time in queueuntil transmission

queuegets full

longerRTT

ratedecreases

queuegets empty

negative feedback

)(1

)( tqB

TtRTT p

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

0

200

400

600

800

1000

1200

1400

1600

1800

2000

10 100 1000


sec

1 flow- hybrid10 flows- hybrid20 flows- hybrid30 flows- hybrid1 flow- ns10 flows- ns20 flows- ns30 flows- ns

• Execution time for 10 minutes of simulation time


Exe

cutio

n tim

e fo

r 10

min

Of s

imul

atio

n tim

e [s

ec]

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

pRTTT

23.1

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 model Simulations

Steady state

Dynamic Fluid Sthochastic Hybrid Fluid Hybrid Packet

Sally96, Padhye99,Yang00, Bansal00

Misra99, Low02, paganini

03

Misra00, Liu03

Bohacek03 Nicol98, Guo00,

Kesidis96, Ahn96

Gu00, Schwetma

n78, Tak01,

Melamed01, Riley02, cameron03,huang99

NS-2, PDNS,Qua

lNet, SSFNET, OPNET

a hybrid systems modeling framework for data communication networks ph.d dissertation proposal...

Documents

hybrid modeltrack packet

3t2nd packet sent3rd

sent2nd packet

automatic simulation

network protocols

time accuracy

larger scale liu03related

data communication networksph