SDS becomes more and more popular, and several companies have announced their product. But the generic standard still has not appeared; most products are only appropriate for their devices and SDS just can integrate a few storages. In this thesis, we will use the OpenStack to build and manage the cloud service, and use software to integrate storage resources include Hadoop HDFS, Ceph and Swift on OpenStack to achieve the concept of SDS. The software used can integrate different storage devices to provide an integrated storage array and build a virtual storage pool; so that users do not feel restrained by the storage devices. Our software platform also provides a web interface for managers to arrange the storage space, administrate users and security settings. For allocation of the storage resources, we make a policy and assign the specific storage array to the machine that acquires the resource according to the policy.

In the last decade, cloud computing has attracted more and more attentions in both industry and academia [1-8]. It deeply changed people's lives due to its inherent advantages, such as on-demand self-service, resource pooling and rapid resource elasticity, etc.. With the services provided by cloud computing, users can upload their requirements via the Internet to a cloud environment and receive responses following postprocessing in the cloud environment. Among these services, cloud storage service is one of the important and indispensable services [9-12]. Cloud storage makes data storage a service in which data is outsourced to a cloud server maintained by a cloud provider. With the service, data could be stored remotely into the cloud efficiently and safely. Thus, this service attracts many people, especially enterprises, due to that it brings appealing benefits, e.g., avoidance of capital expenditure on hardware and software, relief of the burden for storage management, etc. [13-15].

In this work, we mainly apply the cloud infrastructure (IaaS) and virtualization technology to provide the construction services of a green energy management system. First of all, we used MySQL Cluster database technology to build a data storage system which can solve the challenge of large demand. Digital electricity meter data and environmental information are collected efficiently and quickly in the proposed green energy management system. Next, a virtualized user-interface is provided by graphical presentation to facilitate data analysis. Finally, we control the electricity equipment to reduce Power Usage Effectiveness (PUE) and the overall power consumption target-based on this virtualized user-interface of the data analysis.

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