Optimizing Energy Consumption with Task Consolidation in Clouds

Task consolidation is a way to maximize utilization of cloud computing resources. Maximizing resource utilization provides various benefits such as the rationalization of maintenance , IT service customization, and QoS and reliable services. However, maximizing resource utilization does not mean efficient energy use. Much of the literature shows that energy consumption and resource utilization in clouds are highly coupled. Consequently, some of the literature aims to decrease resource utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. In this paper, we present an energy-aware task consolidation (ETC) technique that minimizes energy consumption. ETC achieves this by restricting CPU use below a specified peak threshold. ETC does this by consolidating tasks amongst virtual clusters. In addition, the energy cost model considers network latency when a task migrates to another virtual cluster. To evaluate the performance of ETC we compare it against MaxUtil. MaxUtil is a recently developed greedy algorithm that aims to maximize cloud computing resources. The simulation results show that ETC can significantly reduce power consumption in a cloud system, with 17% improvement over MaxUtil. Cloud computing has recently become popular due to the maturity of related technologies such as network devices, software applications and hardware capacities. Resources in these systems can be widely distributed and the scale of resources involved can range from several servers to an entire data center. To integrate and make good use of resources at various scales, cloud computing needs efficient methods to manage them [4]. Consequently, the focus of much research in recent years has been on how to utilize resources and how to reduce power consumption. One of the key technologies in cloud computing is virtualization. The ability to create virtual machines (VMs) [14] dynamically on demand is a popular solution for managing resources on physical machines. Therefore, many methods [17,18] have been developed that enhance resource utilization such as memory compression, request discrimination, defining threshold for resource usage and task allocation among VMs. Improvements in power consumption, and the relationship between resource usage and energy consumption has also been widely studied [6,10–12,14–18]. Some research aims to improve resource utilization while others aim to reduce energy consumption. The goals of both are to reduce costs for data centers. Due to the large size of many data centers, the financial savings are substantial. Energy consumption varies according to CPU utilization [11]. Higher CPU utilization …