Efficient resource discovery in grid environments

Abstract

Grid Computing has evolved into an important discipline by differentiating itself from distributed computing through an increased focus on resource sharing, coordination, manageability and high performance. Grid computing combines open, shared, geographically distributed and heterogeneous resources to achieve high computational performance. The objective of the Grid Computing is to solve large problems which can not be solved by single CPU by achieving high computing performance by optimal use of geographically distributed heterogeneous idle resources. These resources may belong to different institutions, different domains; may have different usage policies and may pose different requirements on acceptable requests. One of the major challenges in such highly heterogeneous and complex computing environments is to design efficient Resource Discovery algorithms. Resource Discovery strategies in such cases should be efficient, robust, and scalable. Resource heterogeneity domains, dynamic load on resources, task runtime prediction uncertainty, task-to-resource ratio and resource sharing in the grid environment affects application performance. Grid resources are heterogeneous due to differences in hardware components, differences in Grid software environments, and due to the fact different administrative have different policies for sharing the resources. This work mainly focuses on Efficient Resource Discovery with heterogeneous resources . Initially, an in-depth review of existing models, approaches and algorithms has been done. During the literature review, it is observed that query based, agent based, parameter based and ontology based approach are some of the existing approaches. Flooding algorithm, swamping algorithm, name-dropper algorithm and kutten peleg algorithms. Three models; push, pull and push-pull models are currently being used for Resource Discovery process. A comprehensive study of different middleware also has been done.