A Unified Framework for Modeling TCPVegas, TCPSACK, and TCPReno

1
A Unified Framework for Modeling TCP-Vegas,
TCP-SACK, and TCP-Reno

• Adam Wierman Takayuki OsogamiCarnegie Mellon
  University

Jörgen OlsénUppsala University
2
Motivation
TCP Reno
TCP is widely used and the protocol is constantly
evolving.
How will new mechanisms perform?
3
Our goal is

• A framework for modeling TCP that
• Predicts both loss rate and throughput (the
  full operating point)
• Can model the network under on/off traffic
• Can model arbitrary variations of TCP

4
TCP Source Models
Source Model
Loss Rate Round Trip Time (RTT) Delay
Distribution
Throughput

• Renewal Theory
• - Doesnt predict loss
• rate
• - Assume long flows
• Derived for multiple
• TCP variations
• Padhe, Firoiu, Kumar,
• Cardwell, Towsley,
• Vernon,

Our goal is

• Fluid Models
• Doesnt predict loss
• rate
• Arbitrary network
• - Use simplified TCP
• models
• Bonald, Towsley, Misra,
• Altman, Fekete,

• A framework for modeling TCP that
• Predicts both loss rate and throughput
• (the full operating point)
• Can model the network under on/off traffic
• Can model arbitrary variations of TCP

Lots of other work Markovian models, Processor
sharing models, Control theoretic, Mitrani,
Mitra, Roberts, Bonald, Van der Mei,
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Fixed-point Models

• Fixed-point methods
• Can predict the
• full operating point
• Can capture general
• network behavior
• Almost exclusively used
• for TCP Reno / Tahoe
• Misra, Ott, Firoiu,
• Casetti, Meo, Gibbens,
• Bu, Towsley, Roughan,

• Our Model
• Can predict the
• full operating point
• Can model on/off traffic
• Easily adapts to
• arbitrary variations
• of TCP

Our goal is

• A framework for modeling TCP that
• Predicts both loss rate and throughput
• (the full operating point)
• Can model the network under on/off traffic
• Can model arbitrary variations of TCP

6
Our Model
This work generalizes the framework developed by
Casetti and Meo in 01
7
Outline

• Model Overview
• TCP Model
• Application Model
• Network Model

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TCP Reno
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TCP Reno
Congestion Avoidance Infer congestion only
when a loss occurs, otherwise increase window
size on every RTT.
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TCP Reno Model
10
9
8
7
6
5
4
timeout state
3
The states are labeled by window
size. Transitions happen once every RTT (except
at a timeout state).
2
0
1
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TCP Reno
Slow-start The window size is doubled every
RTT as long as no packets are lost. Exit when
threshold window size is reached.
12
TCP Reno Model
10
9
8
7
6
5
4
4
3
The states are labeled by window
size. Transitions happen once every RTT (except
at a timeout state).
2
2
2
0
0
1
1
1
0
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TCP Reno
Fast Retransmit If a loss occurs and can be
recovered from, cut the window size in half. If
the loss cant be recovered from, time-out.
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TCP Reno Model
10
9
8
7
6
5
5
4
4
4
3
3
The states are labeled by window
size. Transitions happen once every RTT (except
at a timeout state).
2
2
2
2
0
0
1
1
1
0
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TCP Reno
Exponential Backoff Consecutive timeouts cause
the timeout length to double.
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TCP Reno Model
10
9
8
7
6
5
5
4
4
4
3
3
The states are labeled by window
size. Transitions happen Once every RTT
(except at timeout states)
2
2
2
2
0
0
1
1
1
0
0
0
0
0
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TCP Comparison
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TCP Vegas
Congestion Avoidance Adjust window size in
response to the current network delay. e.g.
Too much delay causes the window size to be
reduced.
Time
Time
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TCP Vegas Model
10
9
8
7
6
5
5
4
4
4
3
3
The states are labeled by window
size. Transitions happen Once every RTT
(except at timeout states)
2
2
2
2
0
0
1
1
1
0
0
0
0
0
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TCP Vegas
Fast Retransmit Because the delay is used to
adjust window sizes, we need only drop the
window size by ¼ when we can recover from
loss.
Time
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TCP Vegas Model
10
10
9
9
8
8
7
7
7
6
6
6
5
5
5
4
4
4
4
3
3
3
The states are labeled by window
size. Transitions happen Once every RTT
(except at timeout states)
2
2
2
2
2
0
0
0
0
1
1
1
0
1
0
0
0
0
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TCP Vegas
Slow-start Query delay and exit slow-start early
if delay is too high, or if window size threshold
is reached.
Time
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TCP Vegas Model
10
10
9
9
8
8
7
7
7
6
6
6
5
5
5
4
4
4
4
4
3
3
3
The states are labeled by window
size. Transitions happen Once every RTT
(except at timeout states)
2
2
2
2
2
2
2
0
0
0
0
1
1
1
0
1
0
0
0
0
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Outline

• Model Overview
• TCP Model
• Application Model
• Network Model

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Application Model
App
TCP
Network
Idle
Link
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Outline

• Model Overview
• TCP Model
• Application Model
• Network Model

27
Model Overview
This work generalizes the framework developed by
Casetti and Meo in 01
28
Results
External Metrics Throughput Loss Rate
Internal Metrics Time spent in timeout
states Time spent in slow-start Time spent in
fast retransmit states
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Results External
External Metrics Throughput Loss Rate
Vegas
model ns
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Results External
External Metrics Throughput Loss Rate
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Results Internal
Internal Metrics Time spent in timeout
states Time spent in slow-start Time spent in
fast retransmit states
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Results Internal
Internal Metrics Time spent in timeout
states Time spent in slow-start Time spent in
fast retransmit states
Vegas SACK Reno
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Results Internal
Internal Metrics Time spent in timeout
states Time spent in slow-start Time spent in
fast retransmit states
Vegas SACK Reno
0
0
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Contributions

• A framework for modeling TCP that
• Predicts both loss rate and throughput
• (the full operating point)
• Can model the network under on/off traffic
• Can model arbitrary variations of TCP

Our model of TCP Vegas is top-of-the-line.
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A Unified Framework for Modeling TCP-Vegas,
TCP-SACK, and TCP-Reno
The WOO Model

• Adam Wierman Takayuki OsogamiCarnegie Mellon
  University

Jörgen OlsénUppsala University
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TCP SACK
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TCP SACK
Fast Retransmit Uses Selective ACKs to guarantee
that all losses can be recovered from.
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TCP SACK Model
10
9
8
7
6
5
5
4
4
4
3
3
The states are labeled by window
size. Transitions happen Once every RTT
(except at timeout states)
2
2
2
2
0
0
1
1
1
0
0
0
0
0
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Network Model
Network Model
Arrival Rate
Loss Rate Delay Distribution
Bottleneck Buffer
TCP Sending Rate
Link Capacity
Buffer Capacity
What kind of queue? M/D/1/b, M10/M/1/b, M/M/1/b
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Extensions
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Network Models
Network Model
Arrival Rate
Loss Rate Delay Distribution
Lots of techniques from Queueing, Markov chains,
Learning theory, and others