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Traffic Demand Estimation Using Information Theory

Explore how entropy and maximum entropy principle are applied to traffic matrix estimation, with results showing good accuracy and scalability in network environments. Discover the approach's robustness and sensitivity to various data types and network topologies.

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Traffic Demand Estimation Using Information Theory

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  1. Shannon Lab An Information-Theoretic Approach to Traffic Matrix EstimationYin Zhang, Matthew Roughan, Carsten Lund – AT&T ResearchDavid Donoho – Stanford AT&T Labs - Research

  2. Problem Have link traffic measurements Want to know demands from source to destination B C A AT&T Labs - Research

  3. Approach Principle * “Don’t try to estimate something if you don’t have any information about it” • Maximum Entropy • Entropy is a measure of uncertainty • More information = less entropy • To include measurements, maximize entropy subject to the constraints imposed by the data • Impose the fewest assumptions on the results • Instantiation: Maximize “relative entropy” • Minimum Mutual Information AT&T Labs - Research

  4. Results – Single example • ±20% bounds for larger flows • Average error ~11% • Fast (< 5 seconds) • Scales: • O(100) nodes AT&T Labs - Research

  5. Other experiments • Sensitivity • Very insensitive to lambda • Simple approximations work well • Robustness • Missing data • Erroneous link data • Erroneous routing data • Dependence on network topology • Via Rocketfuel network topologies • Additional information • Netflow • Local traffic matrices AT&T Labs - Research

  6. Conclusion • We have a good estimation method • Robust, fast, and scales to required size • Accuracy depends on ratio of unknowns to measurements • Derived from principle • Approach gives some insight into other methods • Why they work – regularization • Should provide better idea of the way forward • Implemented • Used in AT&T’s NA backbone • Accurate enough in practice AT&T Labs - Research

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