Particle based Optimization for Predictive Energy Efficient Data Center Management
Published in 2020 59th IEEE Conference on Decision and Control (CDC), 2020
A.D. Carnerero, D.R. Ramirez, D. Limon, T. Alamo
Data centers are energy-hungry infrastructures that provide cloud computing services. The growing number of data centers in use has led to a drastic increment of the energy consumption associated to these facilities, causing environmental concerns. For that reason, efficient management strategies are needed in order to reduce the energy consumption while the quality of service is kept. This paper presents a unified management approach for the thermal and workload distribution problem in data centers, shaped as a Model Predictive Control problem. The corresponding optimization problem is intractable for conventional solvers because the model is based on multiple queues and the decision variables are a mix of integer and real valued ones. A highly parallelizable particle based optimization algorithm is proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy.