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Optimisation strategies for distributed computing using an adaptive randomised structured network

Fung, C.C. and Li, J.B. (2008) Optimisation strategies for distributed computing using an adaptive randomised structured network. In: 7th International Conference on Machine Learning and Cybernetics, ICMLC, 12-15 July 2008, Kunming.

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    Link to Published Version: http://dx.doi.org/10.1109/ICMLC.2008.4621082
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    Abstract

    One way to improve computational efficiency for complex engineering applications is to utilise distributed computing. In such distributed system, accessing objects through location-independent names can improve the system's transparency, scalability and reliability. Names however need to be resolved prior to passing the messages between the objects. This paper reports an Adaptive RandoMised Structured search network termed ARMS, which utilises a distributed Ant Colony Optimisation algorithms (ACO) to improve the efficiency of searching in a distributed environment. The paper further investigates different kinds of optimisation strategies in order to improve search efficiency. Simulation studies have shown ARMS is superior to Chord, a well-known structured network, under various performance measures.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: (c) 2008 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/633
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