Risk-based Survivable Network Design

by Vajanapoom, Korn

Abstract (Summary)
Communication networks are part of the critical infrastructure upon which society and the economy depends; therefore it is crucial for communication networks to survive failures and physical attacks to provide critical services. Survivability techniques are deployed to ensure the functionality of communication networks in the face of failures. The basic approach for designing survivable networks is that given a survivability technique (e.g., link protection, or path protection) the network is designed to survive a set of predefined failures (e.g., all single-link failures) with minimum cost. However, a hidden assumption in this design approach is that the sufficient monetary funds are available to protect all predefined failures, which might not be the case in practice as network operators may have a limited budget for improving network survivability. To overcome this limitation, this dissertation proposed a new approach for designing survivable networks, namely; risk-based survivable network design, which integrates risk analysis techniques into an incremental network design procedure with budget constraints. In the risk-based design approach, the basic design problem considered is that given a working network and a fixed budget, how best to allocate the budget for deploying a survivability technique in different parts of the network based on the risk. The term risk measures two related quantities: the likelihood of failure or attack, and the amount of damage caused by the failure or attack. Various designs with different risk-based design objectives are considered, for example, minimizing the expected damage, minimizing the maximum damage, and minimizing a measure of the variability of damage that could occur in the network. In this dissertation, a design methodology for the proposed risk-based survivable network design approach is presented. The design problems are formulated as Integer Programming (InP) models; and in order to scale the solution of models, some greedy heuristic solution algorithms are developed. Numerical results and analysis illustrating different risk-based designs are presented.
Bibliographical Information:

Advisor:David Tipper; Prashant Krishnamurthy; Deep Medhi; Richard Thompson; Bjorn Jager

School:University of Pittsburgh

School Location:USA - Pennsylvania

Source Type:Master's Thesis

Keywords:information science


Date of Publication:09/25/2008

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