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Improved Performance of Computer Networks by Embedded Pattern Detection

  • Angel Kuri-Morales
  • Iván Cortés-Arce
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)

Abstract

Computer Networks are usually balanced appealing to personal experience and heuristics, without taking advantage of the behavioral patterns embedded in their operation. In this work we report the application of tools of computational intelligence to find such patterns and take advantage of them to improve the network’s performance. The traditional traffic flow for Computer Network is improved by the concatenated use of the following “tools”: a) Applying intelligent agents, b) Forecasting the traffic flow of the network via Multi-Layer Perceptrons (MLP) and c) Optimizing the forecasted network’s parameters with a genetic algorithm. We discuss the implementation and experimentally show that every consecutive new tool introduced improves the behavior of the network. This incremental improvement can be explained from the characterization of the network’s dynamics as a set of emerging patterns in time.

Keywords

Load Balancing Computer Networks Intelligent Agents Neural Networks Genetic Algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Angel Kuri-Morales
    • 1
  • Iván Cortés-Arce
    • 2
  1. 1.Instituto Tecnológico Autónomo de México, ITAM, D.F.México
  2. 2.Universidad Nacional Autónoma de México, IIMAS, D.F.México

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