Improving the Survivability of a Stochastic Communication Network

Abstract

This research examined the performance of communication networks with stochastically failing components; it also investigated investment strategies to improve network performance. The performance of the network was measured in terms of the amount of information that could by handled. Since the exact calculation of expected maximum flow, which measures the performance of a stochastic network, is mathematically intractable, the bounds of expected maximum flow were investigated. The network performance, both under normal and adverse conditions, was measured analytically by formulating the problem as a linear programming model. The improvement of network performance was made by increasing the capability of the components to handle the information. Mixed integer programming models were developed to determine the investment strategy which maximized the lower bound of expected maximum flow. Because of the complexity involved in developing these models for a large network, a Prolog program was written to generate formulations for all performance and investment strategy models based on the arc-path incidence matrix. Such formulations serve as a direct input file to off-the-shelf mathematical programming packages such as the LP/MIP-83 linear and mixed integer programming system. In this research, three realistic communication networks of various sizes and topologies were analyzed.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA202872

Entities

People

  • Eugene Yim

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Algorithms
  • Artificial Intelligence
  • Communication Networks
  • Communication Systems
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Computers
  • Engineering
  • High Level Languages
  • Integer Programming
  • Linear Programming
  • Mathematical Models
  • Mathematical Programming
  • Operations Research

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Statistical inference.