Self-Aware Workload Forecasting in Data Center Power Prediction

Abstract

The number and scale of data centers are rapidly increasing, due to the growing demand for cloud computing services. Cloud computing infrastructure relies on a massive amount of information and communication technology (ICT) equipment, which consume an enormous amount of power. Power saving and energy optimization have therefore become essential goals for data centers. An enhanced data center energy management system (DEMS) provides a solution for data center power consumption based on its coordinative control of ICT equipment. An efficient power prediction model is essential for such a DEMS because it facilitates the proactive control of ICT equipment and reduces the total power consumption. In this paper, we propose a novel self-aware workload forecasting (SAWF) framework for total power consumption prediction in data centers. It includes three major components. First, there is a feature selection module, which evaluates the importance of variables from all ICT equipment in a data center and dynamically selects the most relevant variables for data input. Second, we propose an accurate and efficient neural network model to forecast future total power consumption. Third, we provide an online error monitoring and model updating module that continuously monitors prediction errors and updates the model when necessary.