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In this paper, we address the problem of accurately modeling the cloud data center energy consumption. As minimizing energy consumption has become a crucial issue for the efficient operation and management of cloud data centers, an energy consumption model plays an important role in cloud datacenter energy management and control. Moreover, such model is essential for guiding energy-aware algorithms, such as resource provisioning policies and virtual machine migration policies. To this end, we propose a holistic cloud data center energy consumption model that is based on the principal component analysis and regression methods. Unlike the exiting approaches that focus on single system component in the datacenter, the proposed approach takes into account the energy consumption of the processing unit, memory, disk, and network interface card as well as the application characteristics. The proposed approach is validated through extensive experiments with the SPECpower benchmark. The experimental results show that the proposed energy consumption model achieves more than 95% prediction accuracy.

Fine-Grained Energy Consumption Model of Servers Based on Task Characteristics in Cloud Data Center