VMeter: Power modelling for virtualized clouds

Datacenters are seeing unprecedented growth in recent years. The energy requirements to operate these large scale facilities are increasing significantly both in terms of operation cost as well as their indirect impact on ecology due to high carbon emissions. There are several ongoing research efforts towards the development of an integrated cloud management system to provide comprehensive online monitoring of resource utilization along with the implementation of power-aware policies to reduce the total energy consumption. However, most of these techniques provide online power monitoring based on the power consumption of a physical node running one or more Virtual Machines (VM). They lack a fine-grained mechanism to profile the power of an individual hosted VM. In this work we present a novel power modelling technique, VMeter, based on online monitoring of system-resources having high correlation with the total power consumption. The monitored system sub-components include: CPU, cache, disk, and DRAM. The proposed model predicts instantaneous power consumption of an individual VM hosted on a physical node besides the full system power consumption. Our model is validated using computationally diverse and industry standard benchmark programs. Our evaluation results show that our model is able to predict instantaneous power with an average mean and median accuracy of 93% and 94%, respectively, against the actual measured power using an externally attached power meter.