Energy accounting for shared virtualized environments under DVFS using PMC-based power models

Virtualized infrastructure providers demand new methods to increase the accuracy of the accounting models used to charge their customers. Future data centers will be composed of many-core systems that will host a large number of virtual machines (VMs) each. While resource utilization accounting can be achieved with existing system tools, energy accounting is a complex task when per-VM granularity is the goal.In this paper, we propose a methodology that brings new opportunities to energy accounting by adding an unprecedented degree of accuracy on the per-VM measurements. We present a system — which leverages CPU and memory power models based in performance monitoring counters (PMCs) — to perform energy accounting in virtualized systems. The contribution of this paper is threefold. First, we show that PMC-based power modeling methods are still valid on virtualized environments. Second, we show that the Dynamic Voltage and Frequency Scaling (DVFS) mechanism, which commonly is used by infrastructure providers to avoid power and thermal emergencies, does not affect the accuracy of the models. And third, we introduce a novel methodology for accounting of energy consumption in virtualized systems. Accounting is done on a per-VM basis, even in the case where multiple VMs are deployed on top of the same physical hardware, bypassing the limitations of per-server aggregated power metering. Overall, the results for an Intel® Core™ 2 Duo show errors in energy estimations <5%. Such an approach brings flexibility to the chargeback models used by service and infrastructure providers. For instance, we are able to detect cases where VMs executed during the same amount of time, present more than 20% differences in energy consumption even only taking into account the consumption of the CPU and the memory.

► Performance Counter based power models validation in virtualized systems.
► The virtualization layer does not introduce interferences in model predictions.
► The DVFS settings of the processor do not affect the models validity.
► Methodology to perform accurate per virtual machine energy accounting.
► Detection and quantification of energy biases between virtual machines.