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dc.contributor.advisorMuriel Médard.en_US
dc.contributor.authorAbdrashitov, Vitalyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-09-17T14:50:23Z
dc.date.available2018-09-17T14:50:23Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117805
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 113-117).en_US
dc.description.abstractIn the recent years, the explosive growth of the data storage demand has made the storage cost a critically important factor in the design of distributed storage systems (DSS). At the same time, optimizing the storage cost is constrained by the reliability requirements. The goal of the thesis is to further study the fundamental limits of maintaining data fault tolerance in a DSS spread across a communication network. Particularly, we focus our attention on performing efficient storage node repair in a redundant erasure-coded storage with a low storage overhead. We consider two operating scenarios of the DSS. First, we consider a clustered scenario, where individual nodes are grouped into clusters representing data centers, storage clouds of different service providers, racks, etc. The network bandwidth within a cluster is assumed to be cheap with respect to the bandwidth between nodes in different clusters. We extend the regenerating codes framework by Dimakis et al. [1] to the clustered topologies, and introduce generalized regenerating codes (GRC), which perform node repair using the helper data both from the local cluster and from other clusters. We show the optimal trade-off between the storage overhead and the inter-cluster repair bandwidth, along with optimal code constructions. In addition, we find the minimal amount of the intra-cluster repair bandwidth required for achieving a given point on the trade-off. Second, we consider a scenario, where the underlying network features a highly varying topology. Such behavior is characteristic for peer-to-peer, content delivery, or ad-hoc mobile networks. Because of the limited and time-varying connectivity, the sources for node repair are scarce. We consider a stochastic model of failures in the storage, which also describes the random and opportunistic nature of selecting the sources for node repair. We show that, even though the repair opportunities are scarce, with a practically high probability, the data can be maintained for a large number of failures and repairs and for the time periods far exceeding a typical lifespan of the data. The thesis also analyzes a random linear network coded (RLNC) approach to operating in such variable networks and demonstrates its high achievable rates, outperforming that of regenerating codes, and robustness in a wide range of model and implementation assumptions and parameters such as code rate, field size, repair bandwidth, node distributions, etc.en_US
dc.description.statementofresponsibilityby Vitaly Abdrashitov.en_US
dc.format.extent126 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleCoding approaches for maintaining data in unreliable network systemsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1051458716en_US


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