Due to the advent of technologies and large resource intensive applications, a large scale distributed and heterogeneous system like grids have emerged as popular platforms. Grid Computing is a kind of distributed computing that involves the integrated and collaborative use of geographically-dispersed resources. Hence, reliable resource sharing is required to process the huge amount of computational jobs across system. So, effective approaches are required for scheduling the jobs and balance the load distribution among the available resources. In this paper, a heuristic approach using Ant Colony Optimization for balanced workload distribution is proposed. In this, ants represent the submitted jobs while the ant's pheromone trail represents the computational capacity of the grid resources. The computational capacity of the resource is updated whenever the job is allocated to or released from it. In nutshell, the overall objective of the proposed Ant Based Heuristic Approach to scheduling & workload distribution (AHSWDG) is to distribute workload equally among the available resources. This research compares the proposed AHSWDG approach with the Random approach on the basis of finish time of the jobs and the utilization of grid resources in the system.
Distributed system is a set of resources interconnected by a network. Grid computing systems are distributed system designed by integrating heterogeneous resources with different characteristics. These heterogeneous computing resources are designed for highly complex programs that require high processing power and huge volume of input data. Large scale applications such as meteorological simulations, data intensive applications etc. can be easily solved in grid environment. The performance of the system can be degraded if the resources are overloaded due to incoming of large no. of jobs. To enhance the system performance, Load Balancing is an important issue in Grid Environment. In this paper we present a hierarchical model for load distribution in Grid Environment with comparison of results on the basis of turnaround time.
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