A Novel Optimization Model Based on the Unification of Proximity and Semantic Similarity in Grid Computing

Abstract

Resources in Grid computing are geographically distributed across the world through a wide area network under various virtual organizations. Due to the distributed nature of the Grid, the selection and allocation of the optimal resources from the available resource are challenging. However, the overall Grid performance depends on the selection of Grid resources for user jobs. A significant amount of effort has been made by proposing various resource discovery algorithms. Current Grid literature reveals that the semantic matching can provide more results compared to syntax matching on available resources, but the selection of poor resources for user jobs can affect the Grid performance. The reason for poor selection is because of the allocation of Grid resources based on First Come First Serve (FCFS) scheme, which reduces the utilization of a domain-based semantic ontology Grid system. To overcome the issue and enhance the Grid performance, we propose a novel optimization model based on Unification of Proximity and Semantic similarity in Grid Computing. The purpose of this optimization model is to get optimized resources for user jobs, so that Grid brokers could select optimum resources in terms of proximity with high semantic relevancy. The proposed model utilizes both semantic and proximity criteria and avoids the resources that are not suitable and faraway from the user locations. The model is designed using GridSim and FreePastry simulation & modeling toolkits. The experimental results have been compared with the (FCFS) allocation scheme that shows that the proposed optimization model statistically significantly outperforms the system with FCFS scheme.