Ebook: Mining and Control of Network Traffic by Computational Intelligence

As other complex systems in social and natural sciences as well as in
engineering, the Internet is hard to understand from a technical point
of view. Packet switched networks defy analytical modeling. The
Internet is an outstanding and challenging case because of its fast
development, unparalleled heterogeneity and the inherent lack of
measurement and monitoring mechanisms in its core conception.

This monograph deals with applications of computational intelligence
methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the
Internet. First, the core building blocks of Internet Science and
other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are
considered: predictive modeling of traffic load as well as
summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.

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As other complex systems in social and natural sciences as well as in
engineering, the Internet is hard to understand from a technical point
of view. Packet switched networks defy analytical modeling. The
Internet is an outstanding and challenging case because of its fast
development, unparalleled heterogeneity and the inherent lack of
measurement and monitoring mechanisms in its core conception.

This monograph deals with applications of computational intelligence
methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the
Internet. First, the core building blocks of Internet Science and
other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are
considered: predictive modeling of traffic load as well as
summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.

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As other complex systems in social and natural sciences as well as in
engineering, the Internet is hard to understand from a technical point
of view. Packet switched networks defy analytical modeling. The
Internet is an outstanding and challenging case because of its fast
development, unparalleled heterogeneity and the inherent lack of
measurement and monitoring mechanisms in its core conception.

This monograph deals with applications of computational intelligence
methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the
Internet. First, the core building blocks of Internet Science and
other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are
considered: predictive modeling of traffic load as well as
summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.

Content:
Front Matter....Pages -
Internet Science....Pages 1-51
Modeling Time Series by Means of Fuzzy Inference Systems....Pages 53-85
Predictive Models of Network Traffic Load....Pages 87-145
Summarization and Analysis of Network Traffic Flow Records....Pages 147-189
Inference Systems for Network Traffic Control....Pages 191-262
Open FPGA-Based Development Platform for Fuzzy Inference Systems....Pages 263-304
Back Matter....Pages -

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As other complex systems in social and natural sciences as well as in
engineering, the Internet is hard to understand from a technical point
of view. Packet switched networks defy analytical modeling. The
Internet is an outstanding and challenging case because of its fast
development, unparalleled heterogeneity and the inherent lack of
measurement and monitoring mechanisms in its core conception.

This monograph deals with applications of computational intelligence
methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the
Internet. First, the core building blocks of Internet Science and
other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are
considered: predictive modeling of traffic load as well as
summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.

Content:
Front Matter....Pages -
Internet Science....Pages 1-51
Modeling Time Series by Means of Fuzzy Inference Systems....Pages 53-85
Predictive Models of Network Traffic Load....Pages 87-145
Summarization and Analysis of Network Traffic Flow Records....Pages 147-189
Inference Systems for Network Traffic Control....Pages 191-262
Open FPGA-Based Development Platform for Fuzzy Inference Systems....Pages 263-304
Back Matter....Pages -
....