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Distributed optimization Grid resource discovery

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Abstract

Grid computing is a framework for large-scale resource sharing and indexing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimization grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the recommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate.

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Authors and Affiliations

  1. Soft computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mohammad Hasanzadeh & Mohammad Reza Meybodi

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  1. Mohammad Hasanzadeh
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  2. Mohammad Reza Meybodi
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Correspondence to Mohammad Hasanzadeh.

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Hasanzadeh, M., Meybodi, M.R. Distributed optimization Grid resource discovery. J Supercomput 71, 87–120 (2015). https://doi.org/10.1007/s11227-014-1289-4

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