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Cloud computing is a novel paradigm, where the limitations of ubiquitous connected devices in terms of computing, data access, networking and storage are solved through the use of cloud infrastructure. The pervasive adoption of cloud computing results in a rising carbon footprint due to the high energy consumption of computing servers. This negatively affects the environment and entails an associated increase in electricity costs and consequently operational costs. Many works proposed scheduling algorithms using software-centric power models in order to predict electric power consumption in underlying data centers and to schedule cloud tasks so as to reduce energy consumption. Linear models which are based on the lowest-and highest-power data points (referred to here as the “Power Endpoints Model” - PEM) and the simple linear regression (SLR) model are the most used in the literature. However, these models have traditionally been evaluated using different environments, experimental setups, workloads, and error calculation formulas. In this paper, a unified classification and evaluation for these linear power models is presented, under unified setup, benchmarking applications, and error formula with the main goal being to achieve an objective comparison. A new power model is proposed, named Locally Corrected Multiple Linear Regression (LC-MLR), in order to increase prediction accuracy. A simulation framework for a cloud energy-aware scheduler is introduced. The framework combines the Energy-Aware Task Scheduling on Cloud Virtual Machines (EATSVM) with the LC-MLR power model, and facilitates performance measurement for cloud data centers. The scheduler with the new power model increases energy efficiency without degrading the qualities of service of the system. The workloads used for performance evaluation and comparisons in this work are generated using a diverse set of applications. The results show that LC-MLR outperforms the most-used models for simulation of power consumption of cloud data centers. The detailed performance analysis is elaborated in the paper.

Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation