Research On Cloud Computing Resourcespool And Parallel Scheduling Model

With the rapid development of the business and user scale in e-commerce, the shortage of traditional procurement management system in resource organizat ion and scheduling gradually emerges. The cloud computing based procurement management system is capable of solving the above problem. In this paper, on the background of the development of procurement management systems under the cloud environment, a cloud computing resource pool and parallel scheduling model is constructed, which utilizes the virtualizat ion technique, the distributed file technique and the resource scheduling technique.Firstly, a cloud computing resource pool and parallel scheduling model is constructed. The hierarchical architecture of the model is given. The abstract organizat ion of resource pool is proposed based on the idea of virtualization and localizat ion. The mutual constraint relations between resource classes are established and detailed descriptions are provided. The techniques of resource discovery and deployment, resource monitoring and transfer in resource pool are illustrated. Then, the resource scheduling architecture and the scheduling management proc edure are presented, and the resource scheduling strategies in c loud computing are analyzed. A resource scheduling algor ithm for comprehensive load balancing is designed based on the load balancing scheduling strategy and parallel scheduling.Secondly, for the validat ion of the cloud computing resource pool and parallel scheduling model, a procurement management system based cloud computing is constructed. The analysis and design of the procurement management system are provided according to the UML modeling technique.At the end of this paper, the cloud computing platform is established by injecting resource pool and parallel scheduling code into Hadoop open source cloud computing framework. The typical procurement management system is implement ed with the WebService and J2 EE technique. And the feasibility of the cloud computing resource pool and parallel scheduling model is verified using the stress test.