Co-Scheduling Computational and Networking Resources in E-Science Optical Grids

Abstract

With e-science applications becoming more and more data-intensive, data is generally generated and stored at different locations and can be divided into independent subsets to be analyzed distributed at many compute locations across an optical grid. It is required to achieve an optimal utilization of optical grid resources. This is generally achieved by minimizing application completion time, which is calculated as the sum of times spent for data transmission and analysis. We propose a Genetic Algorithm (GA) based approach that co-schedules computing and networking resources to achieve this objective. The proposed approach defines a schedule of when to transfer what data subsets to which sites at what times in order to minimize data processing time as well as defining the routes to be used for transferring data subsets to minimize data transfer times. Simulation results show the advantages of the proposed approach in both minimizing the maximum application completion time and reducing the overall genetic algorithm execution time.