Optimized Thermal-Aware Job Scheduling and Control of Data Centers

TL;DR

This paper presents an optimization framework for thermal-aware job scheduling in data centers, aiming to minimize energy use by balancing workload distribution and cooling, validated through simulations.

Contribution

It introduces a novel optimization model and controller design that achieve optimal workload and cooling setpoints without prior workload knowledge.

Findings

Controller converges to optimal setpoints under varying workloads

Simulation results validate the effectiveness of the proposed approach

Reduces cooling energy by optimizing workload distribution

Abstract

Analyzing data centers with thermal-aware optimization techniques is a viable approach to reduce energy consumption of data centers. By taking into account thermal consequences of job placements among the servers of a data center, it is possible to reduce the amount of cooling necessary to keep the servers below a given safe temperature threshold. We set up an optimization problem to analyze and characterize the optimal setpoints for the workload distribution and the supply temperature of the cooling equipment. Furthermore under mild assumptions we design and analyze controllers that drive the data center to the optimal state without knowledge of the current total workload to be handled by the data center. The response of our controller is validated by simulations and convergence to the optimal setpoints is achieved under varying workload conditions.