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dc.contributor.advisorVincent W.S. Chan.en_US
dc.contributor.authorZhang, Lei, Ph. D. Massachusetts Institute of Technology. Department Electrical Engineering and Computer Science, fl. 2014en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-01-15T21:10:18Z
dc.date.available2016-01-15T21:10:18Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/100879
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 175-178).en_US
dc.description.abstractOptical Flow Switching (OFS) that employs agile end-to-end lightpath switching for users with large transactions has been shown to be cost-effective and energy-efficient. However, whether it is possible to coordinate lightpath switching and scheduling at a global scale on a per-session basis, and how the control plane and data plane performance correlate remained un-answered. In this thesis, we have addressed the network management and control aspect of OFS, and designed a network architecture enabling both a scalable control plane and an efficient data plane. We have given an overview of essential network management and control entities and functionalities. We focused on the scheduling problem of OFS because its processing power and generated control traffic increase with traffic demand, network size, and closely correlate with data network architecture, while other routine maintenance type of control plane functionalities contribute either a fixed amount or negligibly to the total efforts. We considered two possible Wide Area Network architectures: meshed or tunneled, and developed a unified model for data plane performance to provide a common platform for the performance comparison of the control plane. The results showed that with aggregation of at least two wavelengths of traffic and allowing about two transactions per wavelength to be scheduled to the future, the tunneled architecture provides comparable data plane performance as the meshed architecture. We have developed a framework to analyze the processing complexity and traffic of the control plane as functions of network architecture, and traffic demand. To guarantee lightpath quality in presence of physical-layer impairments, we developed models for quality of EDFA-amplified optical links and impairment-aware scheduling algorithms for two cases, a) the known worst case of channel quality is when there is no "On" channel in a fiber, and b) detailed channel configuration of a fiber is needed to determine channel quality. Without physical-layer impairments, tunneled architecture reduces control plane traffic and processing complexity by orders of magnitude. With impairment-aware scheduling, detailed channel configuration information reporting leads to heavy control traffic (~250 Gbps/edge); while known worst case and tunneling leads to manageable control traffic (~36 Gbps/edge) and processing power (1-4 i7 CPUs).en_US
dc.description.statementofresponsibilityby Lei Zhang.en_US
dc.format.extent178 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleNetwork management and control of flow-switched optical networks : joint architecture design and analysis of control plane and data plane with physical-layer impairmentsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc933529815en_US


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