Probabilistically Certified Management of Data Centers Using Predictive Control

Abstract

Data centers are facilities with large number of servers providing cloud services. The increasing number of data centers in use along the last years has generated environmental concern due to the immense amounts of energy consumed by them. This also includes some auxiliary services such as the cooling equipment which is known to be very costly. For that reason, efficient data center strategies are needed in order to provide an acceptable Quality of Service (QoS) and suitable temperature for every server while using the least amount of resources possible. This paper presents some strategies to deal with the unified workload and temperature problem that appears in the data center. As the system is modeled as a queue and the control variables have an hybrid nature, some highly parallelizable particle based optimization algorithms are proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy. These simulations also show the improvements obtained from the GPU computing. Finally, a probabilistic evaluation approach is developed in order to provide certificates on the probability of constraint satisfaction without increasing the computational burden of the online problem.