Thermal Profiling and Modeling of Hadoop Clusters Using BigData Applications

Abstract

In this paper, we propose a thermal model called tModel, which projects outlet temperatures from inlet temperatures as well as directly measured multicore temperatures rather than deploying a utilization model. We perform extensive experimentation by varying applications types, their input data sizes, and cluster sizes. We collect inlet, outlet, and multicore temperatures of cluster nodes running a group of big-data applications. The proposed thermal model estimates the outlet air temperature of the nodes to predict cooling costs. We validate the accuracy of our model against data gathered by thermal sensors in our cluster. Our results demonstrate that tModel estimates outlet temperatures of the cluster nodes with much higher accuracy over CPU-utilization based models. We further show that tModel is conducive of estimating the cooling cost of data centers using the predicted outlet temperatures.