Phase-aware predictive thermal modeling for proactive load-balancing of compute clusters

Abstract

The increasing trend of high density computing environments have exacerbated the cooling infrastructure of the modern datacenters which contributes to mounting energy costs due to uncoordinated operation. By integrating information technology and infrastructure management through continuous monitoring, a balance between energy requirements of compute and cooling equipment can be achieved. Building an online thermal profile calculation with certain measure of accuracy is a complex problem due to the number of variables involved. In this paper we propose a phase-aware workload placement scheme that helps in reducing thermal variance in a cluster of compute nodes. We use a phase-aware machine learning approach to forecast server thermal profile which is then used for predicting the cluster-level thermal variance. We leverage Intel Xeon class server platform sensors and machine monitoring capability for fine grained assessment of power, thermal and compute utilization. We are able achieve thermal balance by applying intelligent placement algorithms by predetermining the thermal impact of a variation in workload's utilization on a prospective cluster of server using the forecasted temperature. Results from a prototype implementation on a typical server-cluster environment have demonstrated accurate thermal prediction and significant reduction in thermal variance.