Hybrid adaptive meta-scheduling system for grid computing

When the trend in human culture advances, the problems in their science and engineering is increased. To solve those problems, lot of computing power is needed. Grid is a heterogeneous system that allows sharing of resources. Grid computing is a technology that works what super computer does. Efficient utilization of the grid environment requires large amount of computing power. Scheduling independent jobs to the resources is not an easy task. Meta-scheduling schedules maximum number of jobs to the minimum amount of resources which is a very tedious task. Many scheduling algorithms exist to focus either on the job side or on the resource side. Existing Grid Meta - schedulers either target system-centric metrics, such as utilization and throughput, or prioritize jobs based on utility metrics provided by the users. In the proposed work, to utilize the power of grid completely, both the job and resources are taken into account. Jobs are prioritized based on the common location sum that considers both user and system priority. Resources are prioritized based on the computing power which obtained as the normalized value of the CPU _ available. So the high priority job can be submitted to the corresponding resources based on the computing power and it helps to improve the performance.