One of the fundamental issues in Grid decentralised resource discovery services is high communication overheads that affect the Grid system's performance significantly. The rationale is that Grid resources are geographically distributed across the world through a wide area network under various virtual organisations. To address the issue, a significant amount of effort has been made by proposing various decentralised 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 and communication overheads could be better. However, most of the existing decentralised resource discovery models utilise a domain-based semantic ontology with First Come First Serve (FCFS) basis scheduling for allocating Grid resources that can cause job rejection at run time and can pick resources that are far from the user nodes. As a result, communication overheads of the models are affected as the proximity criterion is not being considered in the selection process. To overcome these issues and enhance the application performance, we propose a Unification of Proximity and Semantic similarity for Appropriate Resource Selection (UPSARS) algorithm in a decentralised resource discovery model by using a sub-domain ontology structure for Grid computing environments. The purpose of this unification is to get optimised resources for user jobs (Gridlets) so that Grid brokers could select optimum resources in terms of proximity with high semantic relevancy. The algorithm considers both semantic and proximity criteria and selects the nearby nodes resources and reduces the communication overheads in terms of proximity and latency. We design and implement the model using the GridSim and the FreePastry simulation and modelling toolkits. The experimental results provide promising outcomes to reduce communication overheads and enhance resource allocation performance.
- decentralised resource discovery
- Grid computing
- proximity, semantic