Load Balancing in Grid Environment using Machine Learning - Innovative Approach

Abstract

ABSTRACT Grid computing creates the illusion of a simple but large and powerful self-managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources which leads to the problem of load balance. The main goal of load balancing is to provide a distributed, low cost, scheme that balances the load across all the processors. To improve the global throughput of Grid resources, effective and efficient load balancing algorithms are fundamentally important. Focus of this paper is on analyzing Load Balancing requirements in a Grid environment and proposing an algorithm with machine learning concepts to find more efficient algorithm. The decisions General Terms Grid Computing, Load balancing, Machine learning, Job migration. 1. INTRODUCTION 1.1 Grid Computing Grid is type of parallel and distributed system that enables the sharing, selection and aggregation of geographically distributed resources dynamically at run time depending on their availability, capability, performance, cost, and user quality-of -self-service requirement. Further we can also identify Grid Computing [1, 2, 4] as, a type of parallel computing that relies on complete computers connected to a network. Grids tend to be more loosely coupled, heterogeneous, and geographically distributed. In Grid computing, the details are abstracted, and the resources are virtualized.