The Research And Implementation Of Cost-Effective Resource Scheduling Mechanism In Cloud Computing Environment

As the development of Internet and Web2.0, the amount of requests that the applications face has increased dramatically and put huge pressure on the local datacenters of service providers. Therefore, service providers need to increase the investment in hardware of their datacenters in order to meet the ever-increasing amount of requests. However, local datacenters have some disadvantages including high cost in hardware investment, difficulties in operation and low resource utilization etc. Under these circumstances, cloud computing emerged as a revolutionary computing paradigm. Cloud computing has some advantages including high reliability, high scalability and pay-as-you-go pricing model etc.In order to reduce the cost and guarantee the quality of their services, more and more of service providers choose to build computing environment based on the infrastructures provided by IaaS (Infrastructure-as-a-Service) providers. VMs (Virtual Machine) that IaaS providers provide differ in computing capacity and pricing model. Different strategies to rent VMs will result in different rental cost, which has become an important issue for service providers. Additionally, requests of parallel applications, which can be abstracted as workflows, are the integral part of requests that service providers deal with. While dealing with this kind of requests, service providers’problem is how to make VM scheduling strategy according to the application’s characteristics in order to achieve the goal of reducing rental cost and guarantee SLA (Service Level Agreement) at the same time.To solve this problem,this paper brings forward theVM scheduling model in cloud computing environment and designs an algorithm for service providers to schedule parallel requests. First, this paper designs single workflow scheduling algorithm and based on which designs multiple workflows scheduling algorithm. Second, we design and implement the VM scheduling module based on CloudSim, and use it to evaluate the performace of the algorithm. Finally, we use real-world parallel requests to test the algorithm and the results demonstrate that our algorithm can effectively reduce the rental cost of VMs as well as ensure the SLA of the services.