Run-time Prediction of Power Consumption for Component Deployments
Proceedings of the 15th IEEE International Conference on Autonomic Computing (ICAC 2018), (September 2018)Abstract
The Power consumption of servers in data centers depends greatly on the software running on each server and how it interacts with the hardware. Different deployments of distributed software components on heterogeneous servers can lead to significant differences in power consumption, depending on the server allocation and the current workload. As workloads and load intensity change, components may be re-deployed or exchanged in order to reduce the power consumption for the current load profile. The decision on which component to place on which server during run-time remains difficult as the power consumption that would result from such a placement remains unknown. Existing work on component deployment optimization at run-time focuses on maximizing performance or considers power in the context of static design time decisions. In this paper, we introduce a model to predict the power consumption of component placements at run-time based on the load and power profile collected for a running distributed application in a heterogeneous environment. In addition, we present a model that enables the use of our approach without dedicated power measurement devices, predicting power consumption based on load intensity and performance counters. We show that we can predict the power consumption of two different distributed web applications with a mean absolute percentage error of 2.21% and with an error of 1.04% when predicting a previously unobserved load level.
