Triple integration optimization techniques in data grid environment using OptorSim simulator

Abstract

Data Grid Environments consist of geographically distributed resources to solve scientific problems and tasks of researchers, scientists and engineers, which are difficult to accomplish by traditional methods based on computer networks. Scheduling and replication are considered some of the most important techniques used in data grid environments, which are used to improve performance and availability to get the best throughput in the shortest possible time. Thus, some algorithms are used for these purposes. Effective scheduling working to reduce the time of implementation of tasks (makespan) of the available resources in the data grid, while replication is working to provide appropriate places or replace similar data to accelerate job execution time. On the other hand, there is another technique, which is important as scheduling and replication, which can be used to reduce the time of implementation for a user request. This technique called Access Pattern, defines the order in which the files are requested for each job to accelerate the completion of the task. Most researchers are focusing on the scheduling, replication, or Access Pattern separately, which leads to variation in the results and gives them unsatisfactory results. The contribution of this paper is present the impact and effect of the triple integration of the three techniques to completing tasks in data grid environments by comparing the results of different algorithms available in the OptorSim simulator.