A sub-domain semantic and proximity-based decentralized resource discovery for Grid Computing

The selection and allocation of optimal resources for Grid user jobs is an open issue. The main reason for this is due to Grid resources are geographically distributed across the world through a wide area network under various virtual organizations. To address the issue, a significant amount of effort has been made by proposing various decentralized overlay algorithms with semantic solutions. Current Grid literature reveals that when semantic features are added into discovery services, the probability of finding resources is enhanced. However, most of the existing decentralized resource discovery models utilize a domain-based semantic ontology with First Come First Serve (FCFS) basis scheduling for allocating of Grid resources that can cause job-rejection at run time. To overcome these issues and enhance the application performance, we propose an UPSARS (Unification of Proximity and Semantic similarity for Appropriate Resource Selection) algorithm in a decentralized resource discovery model by using a sub-domain ontology structure for Grid computing environments. The purpose of this unification 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 algorithm considers both semantic and proximity criteria and selects the nearby nodes resources. We design and implement the model using the GridSim and the FreePastry simulation & modeling toolkits. The experimental results provide promising outcomes to enhance resource allocation performance.