Abstract
Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category.
Highlights
Grid computing emerges to address the need to provide users, such as engineers, scientists, city planners, with more computing power (Foster and Kesselman, 2004)
Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO)
Grid applications are basically categorized in to two (Sinnen, 2007); meta-task applications and DAG applications. The former category describes independent tasks which have no priority relations and data communication among them while the later is otherwise known as workflow applications and describes applications that are represented as Directed Acyclic Graph (DAG), in which the nodes of the graph represent the tasks and the directed edges represent the dependences among the tasks
Summary
Grid computing emerges to address the need to provide users, such as engineers, scientists, city planners, with more computing power (Foster and Kesselman, 2004) It creates a collaborative infrastructure where users with their computational facilities, distributed in different geographical locations, are interconnected via a high-speed internet. The goal of mapping computational tasks to grid resources is to achieve optimal schedule and this problem has been proven to be NP-complete (Ullman, 1975) which means the execution of the algorithms. PSO was originally introduced in (Kennedy and Eberhart, 1995) It is a population-based heuristic strategy for solution search. A scenario of group of birds randomly searching for food in an area demonstrates the PSO process (Raja and Baskar, 2012).
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