Temperature aware power allocation: An optimization framework and case studies

In this paper, we propose a general power model along with a versatile optimization methodology that can be applied to different applications for minimizing service delay while satisfying power budget in data centers. We test our methodology on two totally different applications from both cloud computing and enterprise scenarios. Our solution is novel in that it takes into account the dependence of power consumption on temperature, attributed to temperature-induced changes in leakage current and fan speed. While this dependence is well-known, we are the first to consider it in the context of minimizing service delay. Accordingly, we implement our optimization strategies with Hadoop, Tomcat, and MySQL on a 40-node cluster. The experimental results show that our solution cannot only limit the power consumption to the power budget but also achieves smaller delay against static solutions and temperature oblivious DVFS solutions.