Efficient Method for Parallel Process and Matching of Large Data set in Grid Computing Environment

Abstract

Data management is one of the challenging issues in grid computing and its environments. Because grid computing systems and its applications deals with huge amount of data sets, due to the heterogeneous grid resources that belongs to different organizations and various locations with many access policies. Here To achieve the promising potentials of tremendous distributed resources, useful and capable Scheduling Algorithms are important. Task Scheduling is the mapping of tasks to a selected group of resources which may be distributed in different administrative domains. In this the Parallel Processing of the distributed systems will works using the grid scheduling algorithms. Genetic Algorithm which is a type of scheduling algorithm used for task scheduling to the various resources are working as parallel in the distributed systems.Basically, a Grid scheduler receives applications from Grid users, selects sufficient resources for these applications according to acquired information from the Grid Information Service module, and in conclusion generates application to resource mappings based on assured objective functions and predicted resource performance. Information about the status of available resources is very important for a Grid scheduler to make a proper scheduling, particularly when the heterogeneous and self motivated nature of the Grid is taken into account .The function of the Grid information service is to provide such information to Grid schedulers.

Keywords

• grid computing
• parallel processing
• task scheduling
• heterogeneous resources.