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• A new power-exponent function model based on the CPU utilization rate is put forward. • A hardware-aware CPU power measurement (HCPM) is designed and implemented. • Experimental results show the proposed HCPM is effective and efficient. The energy consumption of cloud servers accounts for about 25% of the total energy of cloud data centers. Reducing and optimizing this energy consumption is thus extremely important in energy saving in cloud data centers. Power model is fundamental in energy efficiency optimization scheduling for cloud computing. However, systems and tools for power measurement in the cloud computing environment are relatively scarce, and power models of cloud servers cannot keep up with the times. Therefore, we propose a new CPU power model named power-exponent function model (PEFM) is proposed, which provides higher accuracy in estimating the CPU power of the latest cloud servers than the current linear, polynomial and power function models. A novel hardware-aware CPU power measurement (HCPM) is also proposed, that can select an appropriate CPU power model through the launch year of CPU without power model training. For validating the efficacy of PEFM and HCPM, a set of experiments including OpenStack cluster experiment, based on a distributed energy meter (DEM) implemented by our team were conducted. The experimental results indicate that the proposed PEFM and HCPM not only improve the accuracy of CPU power estimation in cloud servers in cloud environment, but also reduce the difficulty of model training and simplify system deployment. [ABSTRACT FROM AUTHOR]

Abstract With rapid development of cloud computing technologies and applications, the number and scale of cloud data centers have grown exponentially in recent years. One of the major problems with current cloud data centers is their huge energy consumption, which makes energy consumption management one of the hottest research topics in the field of cloud computing. This paper aims at implementing an effective Distributed Energy Meter (DEM) system for heterogeneous cloud environments based on a multi-component power consumption model for cloud servers. Specifically, we propose a modeling method for the energy consumption of key components (CPU, memory and disk) of computer servers and reveal the mathematical relationship between the resource usage of the key components and the system energy consumption. The proposed DEM system cannot only estimate the energy consumption of heterogeneous cluster environments (Linux and Windows NT), but also support various CPU power consumption models. In addition, a unique disk power consumption model that uses different thresholds to distinguish various disk I/O states (sequential/random, read/write) to achieve an accurate estimation of disk power consumption. Experimental studies conducted on a heterogeneous cluster with workloads generated by PCMark and Sysbench demonstrate that the proposed DEM system outperforms the state-of-art models in estimating the energy consumption of heterogeneous cloud environments. [ABSTRACT FROM AUTHOR]