VPM: Virtual power meter tool for low-power many-core/heterogeneous data center prototypes

Power and energy consumption of data centers are steadily increasing and the work performed by the data centers is not proportional to the power dissipated, where every μA is a revenue for the entity. On the one hand, the hardware community is proposing various methodologies to address this issue such as low-power processors, heterogeneity, etc. to reduce the power of the servers. On the other hand, the software community proposes mechanisms such as virtual machines (VMs), work-load scheduling, etc. to increase the utilization of the processor. In order to properly evaluate the impact of these mechanisms, we need an accurate power monitoring and estimation tool at the hardware host level, the VM level and the system-level. This paper proposes a novel power monitoring middleware on a low-power platform at the node level (ARM Big.LITTLE) and an estimation methodology by using a simulator for future data center prototypes at any given level of virtualization. First, we built an instrumentation framework to measure the power based on hardware counter activities and with respect to current fluctuation. This allows us to build power models for the corresponding platforms, which are fed into the middleware to estimate power on the fly. Furthermore, we used the same framework for future low-power processors such as ARM Cortex-A57 and -A53 based platforms, which are integrated into the architectural simulator by providing an API to estimate power with the power model. Second, we present a machine learning-based energy efficient scheduling of the VMs that leverages VPM. The results obtained with the power monitoring middleware differ less than 2% from real board measurements and 5% when using the simulation environment regardless of the number of virtual machines used. Furthermore, we reduced 40% of energy consumption on average when compared to default scheduling of the KVM hypervisor.