Adaptive MultiThreshold EnergyAware Virtual Machine Consolidation in Cloud Data Center The everincreasing energy consumption in cloud data centers not only translates to high operating costs, but also leads to negative impact on environment. Dynamic consolidation of virtual machine (VM) is proven to be an efficient way to improve resource utilization and reduce energy consumption in cloud data centers. In this paper, both the CPU utilization of system and SLA of users are taken into account to classify hosts and an adaptive multithreshold energyaware virtual machine consolidation algorithm is proposed to provide different consolidation mechanisms for different types of hosts. First, compound threshold is designed for overload hosts and will be adjusted dynamically to ensure both CPU utilization and SLA. Then a QIearning based method is proposed to further divide underload hosts to save energy. Experiment results show that, our proposed algorithm can optimize resource utilization and reduce energy consumption of the data centers while minimizing the SLA violation rate and the number of migrations.

Utilizationpredictionaware virtual machine consolidation approach for energyefficient 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 lowpower 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 (UPPOD) and host underload detection (UPPUD). The future utilization of resources is accurately predicted using a GrayMarkovbased model. In the experiment, the proposed approach is applied for realworld 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.