Combinatorial Double Auction based Meta-scheduler for Medical Image Analysis Application in Grid Environment

Abstract

Grid computing provides more computing power to solve the financial forecasting, weather forecasting, drug design and medical image processing application. Many meta-scheduling algorithms have been proposed to schedule jobs. Considering the architecture and characteristics of the grid environments, traditional meta-scheduler algorithms cannot be applied to the grid computing properly. In this paper, we have come up with a combinatorial double auction based meta-scheduler. The aim of this meta-scheduler is to maximize the number of the job accepted. We assess the proposed meta-scheduler performance by simulating the grid environment. The experimental result shows that the proposed meta-scheduler algorithm maximize the number of the job accepted than the traditional meta-scheduler algorithm.