Runtime data center temperature prediction using Grammatical Evolution techniques

TL;DR

This paper introduces a novel approach using Grammatical Evolution to accurately predict data center temperatures in real-time, reducing reliance on costly CFD simulations and enabling energy-efficient cooling management.

Contribution

The paper presents a new methodology employing Grammatical Evolution for generating temperature prediction models adaptable to various data centers and conditions.

Findings

Prediction errors below 2°C for server temperature

Models trained on real data show high accuracy

Method is computationally efficient for runtime prediction

Abstract

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 Gramatical 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 2 C and 0.5 C in CPU and server inlet temperature respectively.