Multi-objective Evolution Based Dynamic Job Scheduler in Grid

Grid computing is a high performance computing environment to fulfill large-scale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platform. It is used to solve complex computational-intensive problems, and also provide solution to storage-intensive applications with connected storage resources. Scheduling of user jobs properly on the heterogeneous resources is an important task in a grid computing environment. The main goal of scheduling is to maximize resource utilization, minimize waiting time of jobs, reduce energy consumption, minimize cost to the user after satisfying constraints of jobs and resources. We can trade off between the required level of quality of service, the deadline and the budget of user. In this paper, we propose a Multi-objective Evolution-based Dynamic Scheduler in Grid. Our scheduler have used Multi-objective optimization technique using Genetic algorithm with pareto front approach to find efficient schedules. It explores the search space vividly to avoid stagnation and generate near optimal solution. We propose that our scheduler provides a better grip on most features of grid from perspective of grid owner as well as user. Dynamic grid environment has forced us to make it a real time dynamic scheduler. A job grouping technique is proposed for grouping fine-grained jobs and for ease of computation. Experimentation on different data sets and on various parameters revealed effectiveness of multi-objective scheduling criteria and extraction of performance from grid resource.