Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers

Abstract

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee.