Survey of energy consumption optimization methods for cloud video surveillance system

The cloud video surveillance system is a new application of cloud computing. How to optimize energy consumption of the system is just emerging as a hot research problem. In this paper, the related methods of energy consumption optimization, which are widely used either in cloud computing for its computing and storage nodes, or in general video surveillance system, are surveyed and summarized comprehensively. The limitations of applying the methods to the cloud video surveillance system are pointed out after jointly considering the architecture and energy consumption characteristics of the system. On this basis, a monitoring node deployment method with low energy consumption, a task accessing schedule method of virtual machine oriented load balance, and a storage node energy consumption optimization method based mixing static classification and dynamic migration are proposed tentatively to achieve reducing the comprehensive energy consumption of the typical cloud video surveillance system. The methods proposed in this paper will make a referential contribution to the future research for energy consumption optimization of cloud video surveillance system instructively.