a hybrid systems modeling framework for communication network junsoo lee (usc) prof. stephan bohacek...
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A Hybrid Systems Modeling Framework for Communication
Network
Junsoo Lee (USC)
Prof. Stephan BohacekProf. Joao HespanhaProf. Katia Obraczka
Sep 8, 2004
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Motivation• Problems in network research:
– Design, test and analyze protocols (throughput, fairness, security).– Validate designs (scalability, performance).– Tune network/protocol parameters (Queue size, bandwidth, timers, etc.).
• Study of networks and network protocols have used:
– Analytical• Fast• Significant accuracy loss• Only applicable to limited application
– Simulation• Accurate • Long simulation time• Large memory overhead
– Combination
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Publications
• A Hybrid Systems Modeling Framework for Fast and Accurate Simulation of Data Communication Networks. In Proc. SIGMETRICS, June 2003
• Analysis of a TCP hybrid model. In Proc. of the 39th Annual Allerton Conf. on Comm., Contr., and Computing, Oct. 2001
• Hybrid Modeling of TCP Congestion Control. In Maria
Domenica Di Benedetto, Alberto Sangiovanni-Vincentelli, Hybrid Systems: Computation and Control, number 2034 in Lect. Notes in Comput. Science, pages 291-304, Mar. 2001
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Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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Related Work: Packet Model
• Track individual data packets.
• Computationally intensive.
• Complexity depends on the number of events.
• Does not scale to high bandwidths and complex topologies.
• NS-2 (NS00)
• Pdns (Riley99)
• QualNet
• Opnet (Desbrandes93)
• SSFNET
• Dummynet, Nistnet
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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 events.
• 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)
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Our Approach: Hybrid System• Track packet rates for each flow averaged over small time
scales.
• Capture dynamics of each flow
• Explicitly models some discrete events (drops, queues becoming empty, etc.).
• Time accuracy of a few milliseconds (round-trip time).
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Related Work: Other Models
• Hybrid models:– Discrete event + analytical technique (Schetman78).– Packet (foreground) + fluid model (back-ground) (Melamed01).– Packet (edge) + fluid model (backbone) (Riley02, Yu04)) .– Hybrid buffer model (Cameron03).
• Simulation, Sampling & Analysis:– SHRiNK (Psounis03)
• Abstraction:– Abstract Technique (Huang99)– Packet Train (Ahn96)
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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Simple Hybrid Model Example
State 1
State 2?bx
?ax
0:x
transition enabling condition
state reset
[Shaft00]
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Cwnd of TCP
Slow Start
Fast Recovery
Congestion Avoidance
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Cwnd of TCP
Slow StartFast
Recovery
Congestion Avoidance
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Queue Size
Queue Empty
Queue Full
Queue Not Full
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Queue Size
Queue Empty
Queue Not Full
Queue Full
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Dumbbell topology
When i ri exceeds B the queue fills and data is lost (drops)
drop (discrete event)
r1 bps
r2 bps
r3 bpsq( t ) = queue size
queue (temporary storage for data)
f1
f2
f3
f1
f2
f3
rate = B bps
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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
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TCP Sack Congestion Control1. wf (window size)= number of packets that
can remain unacknowledged for by the destination
2. While there are no drops, increase wf by 1 on each RTT
3. When a drop occurs, divide wi by 2Queuing model TCP controllers
drop
RTT
rf
Consider only CA for now for the simplicity
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On-Off UDP model
On:
Off:
transition enabling condition
state reset
?0timt
)exp(: pttim ?0timt
)exp(: qttim
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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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
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Congestion Controlrouting
queue dynamics (Droptail)
sendingrates
drops
out-queuerates
in-queue rates
TCP
RTTs
UDPHSTCP
REDWireless-Droptail
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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:
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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
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Hybrid Queue Modeltransition enabling condition
-queue-not-full
-queue-full
exporteddiscrete event
-queue-empty
Discrete State
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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
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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
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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. wf is initialized to 1 and ssthresh becomes wf /2
7. During fast recovery, data is sent at a rate consistent with a window size of wf /2
8. Duration of fast recovery (RTT) for Tcp-sack
5. Timeout occurs when
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Full TCP: Sack
Timeout, Delay between drop occurrence and detection are considered
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Other Models
TCP-FAST Wireless-Droptail
TCP-NewReno, TCP-Reno, TCP-Tahoe, HSTCP, STCP, RED, Drop-rotation, UDP,etc
Reacts to congestion by using queuing delay in addition to packet loss
Packet loss occurs in queue-empty or queue-not-full because of wireless medium
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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Validation MethodologyCompared simulation results from• ns-2 packet-level simulator.• Hybrid models implemented in Modelica and Shift.
Plots in the following slides refer to test topologies below:
• 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-shapedumbbell
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Validation MethodologyParking-lot
45,90,135,180ms propagation delaysdrop-tail queuing5-500Mbps bottleneck throughput0-10% UDP on/off background traffic from R7 to R80-10% UDP on/off background traffic from R9 to R10
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Slow Start : Dumbbell• Single TCP flow.• 5Mbps bottleneck throughput.• No background traffic.
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4 Flows : Dumbbell• Four competing TCP flows.• 5Mbps bottleneck throughput.• No background traffic.
hybrid model ns-2
Hybrid model accurately captures flow synchronization
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4 Flows with BG:Y-shape
hybrid model
• Four competing TCP flows.• 5 Mbps bottleneck throughput.• 10% UDP background traffic
(exponentially distributedon-off times).
ns-2
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Average Throughput and RTT
• Four competing TCP flows.• 5Mbps bottleneck throughput.• 20 trials with 10 minutes
simulation time.• 10% UDP background traffic
(exponentially distributedon-off times).
Hybrid model accurately captures TCP unfairness in 10% relative error for different propagation delays
• 45,90,135,180ms propagation delays• drop-tail queuing
Thru. 1 Thru. 2 Thru. 3 Thru. 4 RTT1 RTT2 RTT3 RTT4
ns-2 1.913 1.134 0.817 0.668 0.091 0.136 0.182 0.225
hybrid model 1.816 1.162 0.876 0.680 0.093 0.138 0.183 0.228
relative error 5.0% 2.4% 6.7% 1.0% 2.1% 1.5% 0.5% 1.3%
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Average throughput and RTT
Thru. 1 Thru. 2 Thru. 3 Thru. 4 RTT1 RTT2 RTT3 RTT4
relative error (5M) 5.0% 2.4% 6.7% 1.0% 2.1% 1.5% 0.5% 1.3%
relate error (500M) 1.2% 4.1% 9.4% 4.1% 0.5% 1.6% .1% .1%
• Four competing TCP flows• 500Mbps bottleneck throughput.• 5 trials with 133 minutes
simulation time.• 10% UDP background traffic
(exponentially distributedon-off times).
Hybrid model accurately captures TCP unfairness in 10% relative error with 500 Mbps for different propagation delays
• 45,90,135,180ms propagation delays• drop-tail queuing
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Empirical Distribution (5M Y-shape)
hybrid model ns-2
Hybrid model captures distribution of congestion windows and queue size
L1-distance/2 cwnd1 cwnd2 cwnd3 cwnd4 bottleneck queue
Y-shape (5M) .05 .035 .11 .02 .165
Y-shape (500M) .11 .095 .095 .09 .06
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0 2000 4000 6000 8000 100000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-3
P
Cwnd & Queue size
• Four competing TCP flows.• 500Mbps bottleneck throughput.• 10% UDP background traffic
(exponentially distributedon-off times).
hybrid model
0 2000 4000 6000 8000 100000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-3
P
Cwnd & Queue size
ns-2
Hybrid model can reproduce similar probability densities compared to ns-2 for y-shape.
Empirical Distribution (500M Y-shape)
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• Four competing flows.• 500Mbps bottleneck.• 10% UDP background.
0 2000 4000 6000 8000 100000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-3
P
Cwnd & Queue size0 2000 4000 6000 8000 10000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-3
P
Cwnd & Queue size
hybrid model ns-2
Hybrid model can reproduce similar probability densities compared to ns-2 for parking-lot.
Empirical Distribution (500M Parking-lot)
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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Execution Time Speedup: Dumbbell
• ns-2 complexity approximately scales with
• hybrid simulator complexity approximately scales with
number of flows
per-flow throughput
(# packets)
hybrid models are particularly suitable for large, high-bandwidth simulations (satellite, fiber optics, backbone)
0.1
1
10
100
1000
10000
100000
10 100 1000
Bottleneck Bandwidth (Mbps)
Exe
cutio
n T
ime
Spe
edup
1 flow
10 flows
20 flows
30 flows
Exe
cuti
on t
ime
of n
s-2
/ hyb
rid m
odel
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Execution Time Speed-upY-shape and Parking-Lot
Execution speed-ups of parking lot is less than that of y-shape because of frequent discrete transition. 200 times faster with parking-lot and 2.5 Gbps.
0.1
1
10
100
1000
10 100 500 2500
Bottleneck Bandwidth (Mbps)
Exe
cutio
n T
ime
Spe
edup
Y-shape
parking-lot
Exe
cuti
on t
ime
of n
s-2
/ hyb
rid m
odel
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Memory Usage: Y-shape
Hybrid model needs memory for each variable and state whereas ns-2 needs memory for each packet.
1
10
100
1000
10 100 500 1000 2500
Bottleneck Bandwidth (Mbps)
Mem
ory
Usa
ge (
MB
)
NS-2
Hybrid Model
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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Network Description Script (NDS)
• Script similar to OTCL in ns-2.• Easy to reconfigure network. • Example NDS primitives:
– Define• Define qsize 2000
– Node• Node node1 node2
– Link with various drop policies• Link node0 node1 bandwidth q-type q-size q-opt
– Tcp-Sack• TcpSack count start-time finish-time time-opt opt-para src path
– UDP• UDP count start-time finish-time time-opt dist rate on-time off-time
src path
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Example of NDS: Internet2.nds
# NDS script fot internet-2define qsize 250000define qtype Droptaildefine band 10000M
node STTL DNVR KSCY IPLS CHIN NYCM WASH ATLA HSTN LOSA SNVA
link STTL DNVR band 0.0257 qtype qsizelink SNVA DNVR band 0.0250 qtype qsizelink DNVR KSCY band 0.0107 qtype qsize
link HSTN KSCY band 0.0155 qtype qsizelink ATLA IPLS band 0.0110 qtype qsize
TcpSack 10 0 40000 0 0 ATLA IPLS CHINTcpSack 10 0 40000 0 0 HSTN KSCY IPLS CHINTcpSack 10 0 40000 0 0 SNVA DNVR KSCY IPLS CHIN NYCM
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NDS Translator
• Parses and translates Network Description Script into Modelica.
• Written in Perl.
• Reads NDS input file (*.nds) and generates Modelica output file (*.mo).
• Extracts only necessary modules from netlib.mo.
• Creates new module which connects extracted modules.
• Netlib.mo is written in Modelica (TCP variations, RED, Droptail, Drop-rotation, Wireless, Fast-Tcp, HSTCP, Round-robin-combine, functions, connectors, etc).
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Case Study: Abilene Backbone
Src Dest delay
Seattle Denver 25.684
Sunnyvale Denver 25.01
Denver Kansas City 10.674
Kansas City Indianapolis 9.34
Indianapolis Chicago 3.99
Chicago New York 20.464
Sunnyvale Los Angeles 7.772
Los Angeles Houston 31.624
Houston Atlanta 19.756
Atlanta Washington 15.938
Washington New York 4.412
Sunnyvale Seattle 16.852
Houston Kansas City 15.504
Atlanta Indianapolis 10.95
Two-way propagation delay
set Num flows Min RTT Path
One 10 15 ms ATLA to CHIN
Two 10 28.8 ms HSTN to CHIN
Three 10 69.5 ms SNVA to NYCM
TCP traffic on Internet-2
12
3
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Example of NDS: Internet2.nds
# NDS script fot internet-2define qsize 250000define qtype Droptaildefine band 10000M
node STTL DNVR KSCY IPLS CHIN NYCM WASH ATLA HSTN LOSA SNVA
link STTL DNVR band 0.0257 qtype qsizelink SNVA DNVR band 0.0250 qtype qsizelink DNVR KSCY band 0.0107 qtype qsize
link HSTN KSCY band 0.0155 qtype qsizelink ATLA IPLS band 0.0110 qtype qsize
TcpSack 10 0 40000 0 0 ATLA IPLS CHINTcpSack 10 0 40000 0 0 HSTN KSCY IPLS CHINTcpSack 10 0 40000 0 0 SNVA DNVR KSCY IPLS CHIN NYCM
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0
0.5
1
1.5
2
2.5
3
3.5
25000 50000 75000 100000 125000 150000
Buffer Size (packets)
Fairn
ess
Rat
io
FR1,2FR2,3FR1,3
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
25000 50000 75000 100000 125000 150000
Buffer Size (packets)
Rou
nd T
rip T
ime
(ms)
Set 1Set 2Set 3
Results: Abilene Backbone
12
3
• Buffer size between 25,000 and 150,000 packets.
• Throughputs computed by averaging 10 flows in each set.
• Fairness Ratio is large when queue size is small.
• Average round trip time shows queuing delay.
• Further analysis on the fairness ratio can be found in thesis (Section 5.4).
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TCP for High-Speed Network
0 10 20 30 40 50 60 70 800
2
4
6
8
10
12x 10
4
Time (seconds)
Cw
nd (
pack
ets)
TCP-SackSTCP HSTCP FASTTCP
• FAST-TCP, HSTCP, STCP.
• LA to New York with 71.7 ms RTT.
• 10 Gbps bandwidth with buffer size of 5,000 packets.
• TCP-SACK increase window too slow in congestion avoidance.
• HSTCP,STCP use aggressive increase in the congestion avoidance.
• Fast TCP can be in steady state without packet loss.
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6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
2500 5000 10000 30000 60000 90000 120000
Buffer Size (packets)
Thr
oug
hput
(G
bp
s)
TCP- SackFastTCPSTCPHSTCP
Average Throughput
• Average throughput for the first 3,600 seconds
• TCP-Sack shows 76% of throughput when buffer size is 2,500
• TCP-Sack fully utilize when queue size is 90,000
• FAST-TCP’s utilization is 100% when queue is 2,500
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Talk Outline
• Related work
• Simple case studies (e.g., UDP, TCP variations).
• Generalized Hybrid Model Framework
• Validation
• Scalability of Hybrid Systems
• Software Tools
• Conclusion
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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
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Future Work
• Verify and Improve FAST-TCP protocols for high-speed network using hybrid systems
• Study on the impact of bursty background traffic to the TCP fairness in the high-speed network
• Develop new models– Predicting voice-over-IP performance
– Network planning to accommodate voice-over-IP
– Modeling data communication over mobile phone networks
– Effect of congestion to the routing
– Modeling the impact of routing anomalies such as route flap
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Thank You!
http://www-rcf.usc.edu/~junsool/hybrid/
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Comparison of Hybrid Model Simulation Environments
Simulator SHIFT DYMOLA
Language SHIFT MODELICA
Institution UC Berkeley Dynasim
Solver Fixed time-step Fixed/Variable time-step
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
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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
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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
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Hybrid Queue Model (RED)
Random Early Dropactive queuing
stochastic counter-queue-not-full
-queue-full
discrete modes
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Publications• Hybrid Systems Modeling
– A Hybrid Systems Modeling Framework for Fast and Accurate Simulation of Data Communication Networks. In Proc. SIGMETRICS, June 2003
– Analysis of a TCP hybrid model. In Proc. of the 39th Annual Allerton Conf. on Comm., Contr., and Computing, Oct. 2001
– Hybrid Modeling of TCP Congestion Control. In Maria Domenica Di Benedetto, Alberto Sangiovanni-Vincentelli, Hybrid Systems: Computation and Control, number 2034 in Lect. Notes in Comput. Science, pages 291-304, Mar. 2001
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Publications• Stochastic Multipath Routing
– Enhancing security via stochastic routing. In Proc. of the 11th IEEE Int. Conf. on Comput. Communications and Networks
• TCP-PR: – A New TCP for Persistent Packet Reordering-
TCP-PR, Accepted for publication, Transaction on Networking
– TCP-PR: TCP for Persistent Packet Reordering. In Proc. of the IEEE 23rd Int. Conf. on Distributed Computing Systems, pages 222-231, May 200
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High Speed TCP (2)
5.0
loglog
loglog5.0
rate) droppacket :)(( 2
2)(2
LowHigh
Low
WW
WwseHighDecreawb
wpwb
wbwpwwa
The whole point is that a(w) increases and b(w) decreases as cwnd becomes larger.Example: behavior when cwnd = 80,000 packets:
TCP HSTCP
a(w) 1 72
b(w) 0.5 0.1