A Novel Genetic Algorithm for Effective Job Scheduling in Grid Environment

A grid is a set of resources such as CPU, memory, disk, applications, and database distributed over wide area networks and supports large-scale distributed applications. Resources in grid are geographically distributed and linked through Internet, to create virtual supercomputer with vast computing capacity to solve complex problems. Scheduling, resource brokering, and load balancing are the essential functionalities of grid environment. Evolutionary algorithms (EA) operate on a population of potential solutions, applying the principle of survival of the fittest. Genetic algorithms belong to a larger class of EA, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. This paper proposes a scheduling technique based on genetic algorithm to schedule jobs effectively in a grid. The proposed algorithm is tested with different sizes of preemptive job requests, and analysis of results has shown significant improvement in scheduling performance.