Service-oriented Resource Broker for QoS-Guaranteed in Grid Computing System Yichao Yang, Jin Wu, Lei Lang, Yanbo Zhou and Zhili Sun Centre for communication systems research University of Surrey Guildford, United Kingdom

Grid Computing What is Grid Computing? A type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed resources depending on their availability, capability, cost, and user Quality of Service requirements for solving large-scale problems/applications. Challenges of Grid Computing To deliver quality of service: The grid system performance efficiency depend on the how good QoS is provided for different user’s different requirement based on the factor of functions, cost, deadline, budget etc. Enhance real-time & QoS capability in grid middleware to meet the demand of scientific applications. Coordinate resources: It is necessary to have a resource scheduling that provides matching users’ need and resource availability.

Problems of Grid QoS How to provide guaranteed bulk data transmission for user’s QoS requirement. The existing grid computing solution does not consider networks as a key resource to provide efficient services for grid apps.

Grid QoS Problem Solution Propose an end-to-end QoS framework to satisfy grid user’s QoS requirements. Introduce a service-oriented resource broker which can provide guaranteed QoS for grid application over large-scale grid network and optimal resource selection for resource sharing. Implement cost-based resource selection algorithm which can effectively utilize potential combined resources and achieve high user request success rate in dynamic service-oriented Grid.

End-to-End QoS Framework User submit application request Display all resources Grid User access resources data Broker makes resource reservation

Interaction Diagram

Broker architecture

Cost-based Resource Selection Algorithm Job requirement received: If have a job Then List_Static combined resource If no resource available Then Send_the job back If find the available combined static resources, pass step to discovery dynamic resource. On Update If combined resource has been selected Then Update_dynamic combined resource information These combined resource information has been update for selection. On Insert If have available combined resource Then Select_combined resource When the best resource has been selected, then these resources have to be reserved by broker for job execution. On Reservation If combined resource has been selected Then Reservation_combined resource On insert If combined resource has been reserved Then job execution Resource failed On delete_resource If resource not available Then resent the job

Simulation Network Topology

Simulation Result (1/2) ‏

Simulation Result (2/2) ‏

Conclusion End-to-End QoS framework has been defined Fully utilize combined resource to support user’s requirements Implement cost-based resource selection algorithm in Grid Resource Broker Discussion of Simulation Result The higher number of the task, the more processing time saved. The broker reservation scheduling has reduced the total processing time by 15-30% and reduced the average cost of processing by 4% when compared to non broker reservation scheduling.

Q&A