End-to-End Adaptive Congestion Control in TCP/IP Networks

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ISBN: 9781439840573 | 1439840571
Cover: Hardcover
Copyright: 4/24/2012

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This book provides an adaptive control theory perspective on designing congestion controls for packet-switching networks. Relevant to a wide range of disciplines and industries, including the music industry, computers, image trading, and virtual groups, the text extensively discusses source oriented, or end to end, congestion control algorithms. The book empowers readers with clear understanding of the characteristics of packet-switching networks and their effects on system stability and performance. It provides schemes capable of controlling congestion and fairness and presents real-world applications to demonstrate the modeling and control techniques.

List of Figures	p. xiii
List of Tables	p. xix
Preface	p. xxi
Introduction	p. 1
Overview	p. 1
Future Internet	p. 2
Internet Congestion Control	p. 4
Adaptive Congestion Control	p. 8
Background on Computer Networks and Congestion Control	p. 13
Controlled System: The Packet-Switched Network	p. 15
Overview	p. 15
Network Connectivity	p. 17
Links and Nodes	p. 17
Sub-Networks	p. 17
Network Classification	p. 19
LAN Topologies	p. 21
Network Communication	p. 24
Packet Switching	p. 24
Protocols and Layering	p. 26
Internet Architecture	p. 28
Transfer Control Protocol (TCP)	p. 32
User Datagram Protocol (UDP)	p. 37
Internet Protocol (IP)	p. 38
Performance Characteristics	p. 40
Queue Size	p. 40
Throughput	p. 41
Link Utilization	p. 41
Packet Loss Rate	p. 41
Round Trip Time	p. 41
Fairness	p. 42
Applications	p. 43
E-Mail	p. 44
World Wide Web	p. 44
Remote Access	p. 45
File Transfer	p. 45
Streaming Media	p. 46
Internet Telephony (VOIP)	p. 46
Concluding Comments	p. 47
Congestion Issues and TCP	p. 49
Overview	p. 49
Core Issues in Congestion Control	p. 50
TCP: Flow Control and Congestion Control	p. 51
Slow Start	p. 52
Congestion Avoidance	p. 53
Fast Retransmit and Fast Recovery	p. 55
TCP Problems	p. 57
Managing Congestion	p. 59
TCP Friendliness	p. 59
Classification of Congestion Control Protocols	p. 60
Window-Based vs. Rate-Based	p. 60
Unicast vs. Multicast	p. 61
End-to-End vs. Router-Based	p. 62
Concluding Comments	p. 63
Measuring Network Congestion	p. 65
Overview	p. 65
Drop Tail	p. 66
Congestion Early Warning	p. 67
Packet Drop Schemes	p. 68
Packet Marking Schemes	p. 72
Concluding Comments	p. 77
Source-Based Congestion Control Mechanisms	p. 79
Overview	p. 79
Traditional TCP	p. 80
TCP Modifications for Networks with Large Bandwidth Delay Products	p. 81
Scalable TCP (STCP)	p. 82
HighSpeed TCP (HSTCP)	p. 82
BIC	p. 84
CUBIC	p. 85
Delay-Based Congestion Control	p. 86
TCP Vegas	p. 87
FAST TCP	p. 88
Congestion Control for Wireless Networks	p. 89
TCP Westwood	p. 90
TCP Veno	p. 91
Congestion Control for Multimedia Applications	p. 92
Rate Adaptation Protocol (RAP)	p. 92
TFRC	p. 94
Concluding Comments	p. 95
Fluid Flow Model Congestion Control	p. 97
Overview	p. 97
The Fluid Flow Model	p. 98
Network Representation	p. 99
Congestion Control as a Resource Allocation Problem	p. 101
Dual Approach	p. 103
Primal Approach	p. 104
Utility Function Selection	p. 104
Open Issues	p. 106
Stability and Convergence	p. 106
Implementation Constraints	p. 107
Robustness	p. 107
Fairness	p. 108
Concluding Comments	p. 109
Adaptive Congestion Control Framework	p. 111
NNRC: An Adaptive Congestion Control Framework	p. 113
Overview	p. 113
Packet Switching Network System	p. 114
Problem Statement	p. 117
Throughput Improvement	p. 118
NNRC Framework Description	p. 120
Future Path Congestion Level Estimator	p. 121
Feasible Desired Round Trip Time Estimator	p. 122
Rate Control	p. 122
Throughput Control	p. 123
Concluding Comments	p. 123
NNRC: Rate Control Design	p. 125
Overview	p. 125
Feasible Desired Round Trip Time Estimator Design	p. 125
Proof of Lemma 8.1	p. 129
Proof of Lemma 8.2	p. 130
Rate Control Design	p. 132
Guaranteeing Boundness of Transmission Rate	p. 137
Reducing Rate in Congestion	p. 138
Illustrative Example	p. 140
Implementation Details	p. 141
Network Topology	p. 142
Normal Scenario	p. 142
Congestion Avoidance Scenario	p. 143
Concluding Comments	p. 148
NNRC: Throughput and Fairness Guarantees	p. 151
Overview	p. 151
Necessity for Throughput Control	p. 151
Problem Definition	p. 153
Throughput Control Design	p. 154
Guaranteeing Specific Bounds on the Number of Channels	p. 156
Reducing Channels in Congestion	p. 157
Illustrative Example	p. 158
Implementation Details	p. 159
Normal Scenario	p. 160
Congestion Avoidance Scenario	p. 163
Throughput Improvement	p. 163
Concluding Comments	p. 169
NNRC: Performance Evaluation	p. 171
Overview	p. 171
Network Topology	p. 172
Scalability	p. 174
Effect of Maximum Queue Length	p. 174
Effect of Propagation Delays	p. 176
Effect of Bandwidth	p. 178
Dynamic Response of NNRC and FAST TCP	p. 180
Bursty Traffic	p. 182
Re-Routing	p. 183
Non-Constant Number of Sources	p. 186
NNRC and FAST TCP Interfairness	p. 188
Synopsis of Results	p. 199
Concluding Comments	p. 200
User QoS Adaptive Control	p. 203
Overview	p. 203
Application Adaptation Architecture	p. 204
QoS Mapping	p. 204
Application QoS Control Design	p. 205
NNRC Source Enhanced with Application Adaptation	p. 207
Illustrative Example	p. 208
Application Adaptation Implementation Details	p. 209
Simulation Study	p. 210
Concluding Comments	p. 210
Appendices	p. 215
Dynamic Systems and Stability	p. 217
Vectors and Matrices	p. 217
Positive Definite Matrices	p. 219
Signals	p. 221
Functions	p. 223
Continuity	p. 223
Differentiation	p. 224
Convergence	p. 225
Function Properties	p. 226
Dynamic Systems	p. 227
Stability Definitions	p. 229
Boundedness Definitions	p. 231
Stability Tools	p. 232
Neural Networks for Function Approximation	p. 247
General	p. 247
Neural Networks Architectures	p. 249
Multilayer Perceptron (MLP)	p. 250
Radial Basis Function Networks (RBF)	p. 252
High-Order Neural Networks (HONN)	p. 253
Off-Line Training	p. 255
Algorithms	p. 257
Gradient Algorithms	p. 257
Least Squares	p. 261
Backpropagation	p. 262
On-Line Training	p. 263
Filtering Schemes	p. 263
Filtered Error	p. 264
Filtered Regressor	p. 265
Lyapunov-Based Training	p. 266
LIP Case	p. 266
NLIP Case	p. 266
Steepest Descent Training	p. 267
Recursive Least Squares Training	p. 268
Robust On-Line Training	p. 269
Bibliography	p. 273
Index	p. 301
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End-to-End Adaptive Congestion Control in TCP/IP Networks

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