A Novel Approach to Scheduling in Grid Environment: Enhanced Ant Colony Optimizer

In this paper we compared and evaluated the scheduling performances of existing heuristic scheduling algorithms with our proposed Enhanced Ant Colony Optimization (EACO). We take the algorithms for comparison Modified Ant Colony Optimization (MACO), MAX-MIN and RASA scheduling algorithms. We also presented a new job scheduling algorithm based on Resource Aware Scheduling Algorithm (RASA). Our algorithm aims to improve the makespan of the job scheduling, avoiding starvation, proper resource selection and allocation according to the system and network performance in dynamic Grid Environment. We achieved our goal with the help of the proposed simulator Grid Network Listing Tool (GNLT). It performed simulation process in real time environment. Thus, the task scheduling can be performed more effectively by achieving lower makespan time than the existing scheduling algorithms.