Runtime monitoring of software energy hotspots

International audienceGreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. However, none of the existing solutions addresses in-process energy monitoring to provide in-depth analysis of a process energy consumption. In this paper, we therefore report on a fine-grained runtime energy monitoring framework we developed to help developers to diagnose energy hotspots with a better accuracy than the state-of-the-art. Concretely, our approach adopts a 2-layer architecture including OS-level and process-level energy monitoring. OS-level energy monitoring estimates the energy consumption of processes according to different hardware devices (CPU, network card). Process-level energy monitoring focuses on Java-based applications and builds on OS-level energy monitoring to provide an estimation of energy consumption at the granularity of classes and methods. We argue that this per-method analysis of energy consumption provides better insights to the application in order to identify potential energy hotspots. In particular, our preliminary validation demonstrates that we can monitor energy hotspots of Jetty web servers and monitor their variations under stress scenarios

Monitoring energy hotspots in software

International audienceGreen IT has emerged as a discipline concerned with the optimiza- tion of software solutions with regards to their energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. In this paper, we report on a fine-grained runtime energy monitoring framework we developed to help developers to diagnose energy hotspots with a better accuracy.Concretely, our approach adopts a 2-layer architecture including OS-level and process-level energy monitoring. OS-level energy monitoring estimates the energy consumption of processes according to different hardware devices (CPU, network card). Process-level energy monitoring focuses on Java-based applications and builds on OS-level energy monitoring to provide an estimation of energy consumption at the granularity of classes and methods. We argue that this per-method analysis of energy consumption provides better insights to the application in order to identify potential energy hotspots. In particular, our preliminary validation demonstrates that we can monitor energy hotspots of Jetty web servers and monitor their variations under stress scenarios

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Task Activity Vectors: A Novel Metric for Temperature-Aware and Energy-Efficient Scheduling

This thesis introduces the abstraction of the task activity vector to characterize applications by the processor resources they utilize. Based on activity vectors, the thesis introduces scheduling policies for improving the temperature distribution on the processor chip and for increasing energy efficiency by reducing the contention for shared resources of multicore and multithreaded processors

Continuous Performance Benchmarking Framework for ROOT

Foundational software libraries such as ROOT are under intense pressure to avoid software regression, including performance regressions. Continuous performance benchmarking, as a part of continuous integration and other code quality testing, is an industry best-practice to understand how the performance of a software product evolves over time. We present a framework, built from industry best practices and tools, to help to understand ROOT code performance and monitor the efficiency of the code for a several processor architectures. It additionally allows historical performance measurements for ROOT I/O, vectorization and parallelization sub-systems.Comment: 8 pages, 5 figures, CHEP 2018 - 23rd International Conference on Computing in High Energy and Nuclear Physic

e-Surgeon: Diagnosing Energy Leaks of Application Servers

GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to energy consumption. In this domain, most of the state-of-the-art solutions concentrate on coarse-grained approaches to monitor the energy consumption of a device or a process. However, none of the existing solutions addresses in-process energy monitoring to provide in-depth analysis of a process energy consumption. In this paper, we therefore report on a fine-grained real-time energy monitoring framework we developed to diagnose energy leaks with a better accuracy than the state-of-the-art. Concretely, our approach adopts a 2-layer architecture including OS-level and process-level energy monitoring. OS-level energy monitoring estimates the energy consumption of processes according to different hardware devices (CPU, network, memory). Process-level energy monitoring focuses on Java-based applications and builds on OS-level energy monitoring to provide an estimation of energy consumption at the granularity of classes and methods. We argue that this per-method analysis of energy consumption provides better insights to the application in order to identify potential energy leaks. In particular, our preliminary validation demonstrates that we can diagnose energy hotspots of Jetty application servers and monitor their variations when stressing web applications.L'informatique verte a émergé comme une discipline qui s'intéresse à l'optimisation des solutions logicielles en ce qui concerne la consommation d'énergie. Dans ce domaine, la plupart des solutions de l'état de l'art se concentre sur des approches à gros grains pour contrôler la consommation énergétique d'un matériel ou un processus. Toutefois, aucune des solutions existantes gère la surveillance au niveau processus afin de fournir une analyse en profondeur de la consommation énergétique d'un processus. Dans ce papier, nous proposons un canevas logiciel à grain fin pour surveiller en temps réel la consommation énergétique des applications, et pour diagnostiquer les fuites d'énergie avec une meilleure précision que l'état de l'art. En particulier, notre approche adopte une architecture à 2 couches, une au niveau du système d'exploitation et le suivi de l'énergie au niveau des processus. La couche de surveillance de l'énergie au niveau de l'OS estime la consommation énergétique au niveau du processus selon différents périphériques matériels (processeur, réseau, mémoire). La couche de surveillance de l'énergie au niveau des processus se concentre sur les applications Java et s'appuie sur la couche OS pour fournir une estimation de la consommation d'énergie à la granularité des classes et méthodes. Nous soutenons que cette analyse au niveau des méthodes de la consommation énergétique fournit un meilleur aperçu de l'application afin d'identifier les fuites énergétiques potentielles. En particulier, nos expériences démontrent que nous pouvons diagnostiquer les hotspots énergétique des serveurs d'application Jetty et de surveiller leurs variations lorsque nous mettons sous pression les applications web

An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking

Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform