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Eytan Modiano. Johnston, Matthew R. (Matthew Ryan) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2011-04-25T16:00:56Z 2011-04-25T16:00:56Z 2010 2010 http://hdl.handle.net/1721.1/62451 711102072 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 91-93). This thesis addresses the problem of logical topology design for optical backbone networks subject to traffic following a Gaussian distribution. The network design problem is broken into three tasks: traffic routing, capacity allocation, and link placement. The routing and capacity allocation problems are formulated as a convex mathematical program. To extend this formulation to discrete optimization problems, such as the link placement sub-problem, it is reformulated as a mixed integer linear program (MILP) by extending tools from robust optimization to Gaussian variables. Bounds are presented to relate capacity allocation to the probability of traffic overflow on a link. Lastly, the link placement subproblem is formulated as an MILP and network topologies for deterministic traffic are compared with those for stochastic traffic. Additionally, this thesis presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for large-scale networks, and we present simulation results to verify our analysis on optimal backup networks. by Matthew R. Johnston. S.M. 93 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. A robust optimization approach to network design Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d7b62035-383e-4f18-81a9-05708553099a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A robust optimization approach to network design</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Johnston, Matthew R. (Matthew Ryan)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis addresses the problem of logical topology design for optical backbone networks subject to traffic following a Gaussian distribution. The network design problem is broken into three tasks: traffic routing, capacity allocation, and link placement. The routing and capacity allocation problems are formulated as a convex mathematical program. To extend this formulation to discrete optimization problems, such as the link placement sub-problem, it is reformulated as a mixed integer linear program (MILP) by extending tools from robust optimization to Gaussian variables. Bounds are presented to relate capacity allocation to the probability of traffic overflow on a link. Lastly, the link placement subproblem is formulated as an MILP and network topologies for deterministic traffic are compared with those for stochastic traffic. Additionally, this thesis presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning. We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for large-scale networks, and we present simulation results to verify our analysis on optimal backup networks.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>