A Combined Frequency Scaling and Application Elasticity Approach for Energy-Efficient Virtualized Data Centers

At present, large-scale data centers are typically over-provisioned in order to handle peak load requirements. The resulting low utilization of resources contribute to a huge amounts of power consumption in data centers. The effects of high power consumption manifest in a high operational cost in data centers and carbon footprints to the environment. Therefore, the management solutions for large-scale data centers must be designed to effectively take power consumption into account. In this work, we combine three management techniques that can be used to control systems in an energy-efficient manner: changing the number of virtual machines, changing the number of cores, and scaling the CPU frequencies. The proposed system consists of a controller that combines feedback and feedforward information to determine a configuration that minimizes power consumption while meeting the performance target. The controller can also be configured to accomplish power minimization in a stable manner, without causing large oscillations in the resource allocations. Our experimental evaluation based on the Sysbench benchmark combined with workload traces from production systems shows that our approach achieves the lowest energy consumption among the compared three approaches while meeting the performance target