CINERGY: Reasoning over the Worst Case Power Consumption of Cloud Virtual Machines

Energy consumption has become a critical concern in Information and Communication Technologies (ICT), pressing for more accurate measurements. While the power consumption of physical servers can be physically monitored, organizations are increasingly adopting virtual environments, such as cloud computing, rendering physical measurements impractical in operational contexts. The state-of-the-art approaches to estimating this 'virtual' consumption mostly consist of assigning server power consumption shares among hosted processes, guided by various system metrics. Unfortunately, such a bottom-up approach is highly sensitive in a multi-tenant environment, thus failing to report stable measurements to stakeholders. For example, the same activity performed by one Virtual Machine (VM) may lead to different power consumption traces, depending on the activity of the co-hosted VMs. As cloud customers have only control over their provisioned virtual resources, we propose a new method to model the power consumption of their virtual appliances, enabling contextagnostic tracking of their environmental impact. This framework, called CINERGY, is designed to be more predictable than the state-of-the-art power models, while still exposing the gains from consolidation. We evaluate its accuracy against ground-truth measurements, often lacking in the literature. We show that CINERGY is deterministic and accurate, with an average error of 6.6%.