An Energy-Aware Workload Balancing Method for Cloud Video Data Storage Management

摘要

The data centers of cloud video surveillance (CVS) systems based on Hadoop have a couple of common problems such as large energy consumption, low power utilization, etc. Addressing to the issue of reducing energy consumption while guaranteeing quality of service, we propose an energy-aware workload balancing method for efficient data storage management in cloud video data centers. A dynamic adjusting algorithm is designed to control the running status of nodes in the data centers according to the access frequency of video data blocks for the purpose of reducing the energy consumption, in order to eliminate the potential influence on the service quality posed by the changes of running status of nodes, the workload balancing between nodes in ring network topology is executed, a nonlinear programming model is established to obtain the minimum number of data blocks transferred during the workload balancing. Experimental results in the GridSim simulation environment show that the proposed method can achieve more energy saving and better performance than the original Hadoop.