Adaptive task scheduling based on Multi Criterion Ant Colony Optimization in computational grids

Grid Computing is a form of distributed computing environment that allows sharing, selection and coordinated use of diverse resources owned by different organizations. Effective and efficient scheduling of resources in grid is fundamentally important. To optimize the scheduling of tasks to suitable resources in computational grids, a Multi Criterion Ant Colony Optimization (MCACO) Algorithm is proposed. From the experimental results and analysis it is evaluated that the adaptive task scheduling using MCACO has a faster convergence in task scheduling with minimal makespan and flowtime.

I. Introduction

A computational grid [1] is the term adopted for infrastructure that enables the increase in computation. It provides inexpensive and consistent access to high-end computational resources. To achieve the promising potentials of computational grids, job scheduling is an important issue to be considered. The task scheduling problem has been demonstrated to be a NP-hard optimization problem because of complex characteristics of Grid. So there is a need of an effective job scheduling technique for solving complex computational problems found in grid environment.