Power estimation of software processes provides critical indicators to drive scheduling or power capping heuristics. State-of-the-art solutions can perform coarse-grained power estimation in virtualized environments, typically treating virtual machines (VMs) as a black box. Yet, VM-based systems are nowadays commonly used to host multiple applications for cost savings and better use of energy by sharing common resources and assets.In this paper, we propose a fine-grained monitoring middleware providing real-time and accurate power estimation of software processes running at any level of virtualization in a system. In particular, our solution automatically learns an application-agnostic power model, which can be used to estimate the power consumption of applications.Our middleware implementation, named BITWATTS, builds on a distributed actor implementation to collect process usage and infer fine-grained power consumption without imposing any hardware investment (e.g., power meters). BITWATTS instances use high-throughput communication channels to spread the power consumption across the VM levels and between machines. Our experiments, based on CPU-and memory-intensive benchmarks running on different hardware setups, demonstrate that BITWATTS scales both in number of monitored processes and virtualization levels. This non-invasive monitoring solution therefore paves the way for scalable energy accounting that takes into account the dynamic nature of virtualized environments.

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Abstract-The costs of current data centers are mostly driven by their energy consumption (specifically by the air conditioning, computing and networking infrastructure). Yet, current pricing models are usually static and rarely consider the facilities' energy consumption per user. The challenge is to provide a fair and predictable model to attribute the overall energy costs per virtual machine (VM). Current pay-as-you-go models of Cloud providers allow users to easily know how much their computing will cost. However, this model is not fully transparent as to where the costs come from (e.g., energy). In this paper we introduce EPAVE, a model for Energy-Proportional Accounting in VMbased Environments. EPAVE allows transparent, reproducible and predictive cost calculation for users and for Cloud providers. We show these characteristics of EPAVE by a number of use cases in heterogeneous data centers and discuss the applicability of EPAVE.

Software power estimation of CPUs is a central concern for energy efficiency and resource management in data centers. Over the last few years, a dozen of ad hoc power models have been proposed to cope with the wide diversity and the growing complexity of modern CPU architectures. However, most of these CPU power models rely on a thorough expertise of the targeted architectures, thus leading to the design of hardware-specific solutions that can hardly be ported beyond the initial settings. In this article, we rather propose a novel toolkit that uses a configurable/interchangeable learning technique to automatically learn the power model of a CPU, independently of the features and the complexity it exhibits. In particular, our learning approach automatically explores the space of hardware performance counters made available by a given CPU to isolate the ones that are best correlated to the power consumption of the host, and then infers a power model from the selected counters. Based on a middleware toolkit devoted to the implementation of software-defined power meters, we implement the proposed approach to generate CPU power models for a wide diversity of CPU architectures (including Intel, ARM, and AMD processors), and using a large variety of both CPU and memoryintensive workloads. We show that the CPU power models generated by our middleware toolkit estimate the power consumption of the whole CPU or individual processes with an accuracy of 98.5% on average, thus competing with the state-of-the-art power models.

Major challenges for the transition of power systems do not only tackle power electronics but also communication technology, power market economy and user acceptance studies. Simulation is an important research method therein, as it helps to avoid costly failures. A common smart grid simulation platform is still missing. We introduce a conceptual model of agents in multiple flow networks. Flow networks extend the depth of established power flow analysis through use of networks of information flow and financial transactions. We use this model as a basis for comparing different power system simulators. Furthermore, a quantitative comparison of simulators is done to facilitate the decision for a suitable tool in comprehensive smart grid simulation.