Towards Power Consumption Modeling for Servers at Scale

As of 2010 data centers use 1.5% of global electricity production and this is expected to keep growing [1]. There is a need for a near real-time power consumption modeling/monitoring system that could be used at scale within a Software Defined Data Center (SDDC). The power consumption models and information they provide can then be used to make better decisions for data center orchestration, e.g., whether to migrate virtual machines to reduce power consumption. We propose a scalable system that would 1) create initial power consumption models, as needed, for data center components, and 2) could be continually refined while the components are in use. The models will be used for the near real-time monitoring of power consumption, as well as predicting power consumption before and after potential orchestration decisions. The first step towards this goal of whole data center power modeling and prediction is to be able to predict the power consumption of one server effectively, based on high level utilization statistics from that server. In this paper we present a novel method for modeling whole system power consumption for a server, under varying random levels of CPU utilization, with a scalable random forest based model, that utilizes statistics available at the data center management level.