Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm

Abraham, Ajith; Liu, Hongbo; Zhang, Weishi; Chang, Tae‐Gyu

doi:10.1007/11893004_65

Cited by 55 publications

(55 citation statements)

References 3 publications

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“…Hongbo Liu et al [2] proposed a Fuzzy Particle Swarm Optimization (FPSO) for scheduling the job. This optimization algorithm dynamically generates an optimal schedule so has to complete the jobs within a minimal period of time as well as utilizing the resource in an effective fashion.…”

Section: Related Workmentioning

confidence: 99%

Optimized Resource Provisioning Method for Computational Grid

Suresh¹,

Keerthana²

2016

IJGCA

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Section: Related Workmentioning

confidence: 99%

Optimized Resource Provisioning Method for Computational Grid

Suresh¹,

Keerthana²

2016

IJGCA

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“…An ACO implementation for the problem under ETC model has been reported by Ritchie [7]. Abraham et al [2] proposed an approach using fuzzy PSO algorithm.…”

Section: Swarm Intelligence Methods Ant Colony Optimization (Aco) Andmentioning

confidence: 99%

A Compendium of Heuristic Methods for Scheduling in Computational Grids

Xhafa

Abraham

2009

Intelligent Data Engineering and Automated Learning - IDEAL 2009

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Abstract. Scheduling in large scale distributed computing environments such as Computational Grids, is currently receiving a considerable attention of researchers. Despite that scheduling in such systems has much in common with scheduling in traditional distributing systems, the new characteristics of Grid systems make the problem more complex and versatile to match different needs of Grid-enabled applications. In this work, by conceiving scheduling problem as a family of problems, we first identify most common versions of the scheduling problem based on six dimensions: type of the environment, architecture type of the scheduler, immediacy of the processing, type of interrelations among tasks, type of preemptive policy and type of optimization model. Then, we review different families of heuristic methods used for the resolution of the problem, including ad hoc methods, local search methods and population-based methods.

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“…[18] Used the PSO for scheduling workflow applications in distributed environment. [19] used a fuzzy particle swarm optimization and Izakian et al [2] used discrete version of particle swarm optimization for grid scheduling. Another method for improving the quality of solution is hybridization with local search techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

Garg¹,

Singh²

2013

IJCNIS

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-The problem of scheduling dependent tasks (DAG) is an important version of scheduling, to efficiently exploit the computational capabilities of grid systems. The problem of scheduling tasks of a graph onto a set of different machines is an NP Complete problem. As a result, a number of heuristic and meta-heuristic approaches are used over the years due to their ability of providing high quality solutions with reasonable computation time. Discrete Particle Swarm Optimization is one such meta-heuristic used for solving the discrete problem of grid scheduling, but this method converge to sub optimal solutions due to premature convergence. To deal with premature convergence, in this paper we proposed the design and implementation of hierarchical discrete particle swarm optimization (H-DPSO) for dependent task scheduling in grid environment. In H-DPSO particles are arranged in dynamic hierarchy where good particles lying above in hierarchy are having larger influence on the swarm. We consider the bi-objective version of problem to minimize makespan and total cost simultaneously as the optimization criteria. The H-DPSO based scheduler was evaluated under different application task graphs. Simulation analysis manifests that H-DPSO based scheduling is highly viable and effective approach for grid computing.

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Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm

Cited by 55 publications

References 3 publications

Optimized Resource Provisioning Method for Computational Grid

Optimized Resource Provisioning Method for Computational Grid

A Compendium of Heuristic Methods for Scheduling in Computational Grids

Enhancing the Discrete Particle Swarm Optimization based Workflow Grid Scheduling using Hierarchical Structure

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