Runtime data center temperature prediction using Grammatical Evolution techniques.

Graphical abstractDisplay Omitted HighlightsModeling methodology for temperature prediction in data centers.Prediction of server CPU and inlet temperature under variable cooling setups.Development of time-dependent multi-variable models based on Grammatical Evolution.Premature convergence techniques using Social Disaster Techniques and Random Off-Spring Generation.Comparison to other techniques such as ARMA, N4SID and NARX.Models tuned, trained and tested using measurements from real server and data center traces. Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Grammatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2C and 0.5C in CPU and server inlet temperature respectively.