Research On Optimizing Technology Of Resource Management In Cloud Environment

In recent years, cloud computing has drawn more and more attention due to the advantages such as dynamic resource allocation, elastic extension, resource sharing and pay-on-demand. Not only has cloud computing changed the framework mode of today’s IT infrastructure, it has also changed the way how cloud users obtain IT resources, as well as how cloud service provider manages and provides software, technology and solutions.While cloud computing brings benefits to enterprises and users, it at the same time presents more challenges to the administrators of the cloud platform. For an IaaS cloud, the primary task is to satisfy users’ demands of resources. Besides, administrators also have to deal with problems such as how to optimize allocation and utilization of resources at the data center level, how to guarantee application’s performance and scalability and how to cut costs of maintenance and management.This paper focuses on the runtime optimization of IaaS private clouds. In such an environment, administrators usually have more autonomy and control over cloud resources and applications, which brings more space for optimization. In order to achieve appropriate resource allocation for virtual machines and improve utilization, this paper first proposes a workload-aware method to optimize the resource configuration of virtual machines, as well as an allocation scheme based on resource reservation and sharing, which can derive the proper amount of resources to meet the real needs of virtual machines. To cope with the fluctuating workload in cloud computing, this paper also proposes a method to dynamically adjust the resource allocation for a multiple-layer application, concerning when and how to perform the adjustment. Integrating the methods mentioned above, this paper arrives at a systematic framework for the runtime optimization of cloud resource management. This paper uses a mainstream benchmark RUBiS application in this field to test the framework, and experimental results verified the correctness and effectiveness of the schemes of resource allocation optimization for virtual machines and dynamic adjustment for multiple-layer applications.