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    SCALABLE TECHNIQUES FOR FAILURE RECOVERY AND LOCALIZATION

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    Author
    Cho, Sangman
    Issue Date
    2011
    Keywords
    Electrical & Computer Engineering
    Advisor
    Ramasubramanian, Srinivasan
    
    Metadata
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Failure localization and recovery is one of the most important issues in network management to provide continuous connectivity to users. In this dissertation, we develop several algorithms for network failure localization and recovery. First, to achieve resilient multipath routing we introduce the concept of Independent Directed Acyclic Graphs (IDAGs). Link-independent (Node-independent) DAGs satisfy the property that any path from a source to the root on one DAG is link-disjoint (node- disjoint) with any path from the source to the root on the other DAG. Given a network, we develop polynomial time algorithms to compute link-independent and node-independent DAGs. The algorithm developed in this dissertation: (1) provides multipath routing; (2) utilizes all possible edges; (3) guarantees recovery from single link failure; and (4) achieves all these with at most one bit per packet as overhead when routing is based on destination address and incoming edge. We show the effectiveness of the proposed IDAGs approach by comparing key performance indices to that of the independent trees and multiple pairs of independent trees techniques through extensive simulations. Secondly, we introduce the concept of monitoring tours (m-tours) to uniquely localize all possible failures up to k links in arbitrary all-optical networks. We establish paths and cycles that can traverse the same link at most twice (backward and forward) and call them m-tours. An m-tour is different from other existing schemes such as m-cycle and m-trail which traverse a link at most once. Closed (open) m-tours start and terminate at the same (distinct) monitor location(s). Each tour is constructed such that any shared risk linked group (SRLG) failure results in the failure of a unique combination of closed and open m-tours. We prove k-edge connectivity is a sufficient condition to localize all SRLG failures with up to k-link failures when only one monitor station is employed. We introduce an integer linear program (ILP) and a greedy scheme to find the placement of monitoring locations to uniquely localize any SRLG failures with up to k links. We provide a heuristic scheme to compute m-tours for a given network. We demonstrate the validity of the proposed monitoring method through simulations. We show that our approach using m-tours significantly reduces the number of required monitoring locations and contributes to reducing monitoring cost and network management complexity through these simulation results. Finally, this dissertation studies the problem of uniquely localizing single network element failures involving a link/node using monitoring cycles, paths, and tours. A monitoring cycle starts and ends at the same monitoring node. A monitoring path starts and ends at distinct monitoring nodes. A monitoring tour starts and ends at a monitoring station, however may traverse a link twice, once in each direction. The failure of any link/node results in the failure of a unique combination of cycles/paths/tours. We develop the necessary theories for monitoring single element (link/node) failures using only one monitoring station and cycles/tours respectively. We show that the scheme employing monitoring tours can decrease the number of monitors required compared to the scheme employing monitoring cycles and paths. With the efficient monitoring approach that uses monitoring tours, the problem of localizing up to k element (link/node) failures using only single monitor is also considered. Through the simulations, we verify the effectiveness of our monitoring algorithms.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
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