Certain investigations on swarm intelligence based algorithms for performance enhancement in grid task scheduling

Abstract

Grid computing is an emerging field for the next generation parallel and distributed computing platform for solving large scale computational and data intensive problems in science engineering and commerce It enables sharing selection and aggregation of a wide variety of geographically distributed resources including supercomputers databases data sources and specialized devices owned by different organizations Authentication newlineauthorization secure and reliable file transfer distributed storage management and resource scheduling across organizational boundaries are the list of problems that need to be solved in grid computing Grid users need not be aware of the computational resources that are used for executing their applications and storing their data Adaptive resource management and scheduling are the present technical challenges in the grid Another newlinechallenging task of grid computing is failure handling in grid scheduling The main objective of grid scheduling is to find a feasible schedule that minimizes the make span e completion time required to finish all tasks in the job pool In recent years much attention has been given to solve the grid scheduling problem by using heuristic approaches such as Ant Colony Optimization local search tabu search genetic and Particle Swarm Optimization algorithms newline newline