A Low Energy Consumption Storage Method for Cloud Video Surveillance Data Based on SLA Classification

Abstract

With the continuous expansion of the amount of data with time in mobile video applications such as cloud video surveillance (CVS), the increasing energy consumption in video data centers has drawn widespread attention for the past several years. Addressing the issue of reducing energy consumption, we propose a low energy consumption storage method specially designed for CVS systems based onthe service level agreement (SLA) classification. A novel SLA with an extra parameter of access time period is proposed and then utilized as a criterion for dividing virtual machines (VMs) and data storage nodes into different classifications. Tasks can be scheduled in real time for running on the homologous VMs and data storage nodes according to their access time periods. Any nodes whose access time periods do not encompass the current time will be placed into the energy saving state while others are in normal state with the capability of undertaking tasks. As a result, overall electric energy consumption in data centers is reduced while the SLA is fulfilled. To evaluate the performance, we compare the method with two related approaches using the Hadoop Distributed File System (HDFS). The results show the superiority and effectiveness of our method.