With the increase of demand for computing resources and the struggle to provide the necessary energy, power-aware resource management is becoming a major issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer's resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different from the others. We argue that the first step towards properly managing power is to deeply understand the power consumption of the workload, which involves predicting the workload power consumption and exploiting it by using smart power-aware scheduling algorithms. Crucial questions are (i) how sophisticated a prediction method needs to be to provide accurate workload power predictions, and (ii) to what point an accurate workload's power prediction translates into efficient power management. In this work, we proposed a method to predict and exploit HPC workloads power consumption with the objective of reducing the supercomputers power consumption, while maintaining the management (scheduling) performance of the RJMS. Our method exploits workload submission logs with power monitoring data, and relies on a mix of lightweight power prediction methods and a classical EASY Backfillling inspired heuristic. Then, we model and solve the power capping scheduling as a greedy knapsack algorithm. This algorithm improves the Quality of Service and avoids starvation while keeping the solution lightweight. We base this study on logs of Marconi 100, a 980-node supercomputer. We show using simulation that a lightweight history-based prediction method can provide accurate enough power prediction to improve the energy management of a large scale supercomputer compared to energy-unaware scheduling algorithms. These improvements have no significant negative impact on performance.

With the increase of demand for computing resources and the struggle to provide the necessary energy, power-aware resource management is becoming a major issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer’s resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different from the others. We argue that the first step toward properly managing energy is to deeply understand the energy consumption of the workload, which involves predicting the workload’s power consumption and exploiting it by using smart power-aware scheduling algorithms. Crucial questions are (i) how sophisticated a prediction method needs to be to provide accurate workload power predictions, and (ii) to what point an accurate workload’s power prediction translates into efficient energy management. In this work, we propose a method to predict and exploit HPC workloads’ power consumption, with the objective of reducing the supercomputer’s power consumption while maintaining the management (scheduling) performance of the RJMS. Our method exploits workload submission logs with power monitoring data, and relies on a mix of light-weight power prediction methods and a classical EASY Backfillling inspired heuristic. We base this study on logs of Marconi 100, a 980 servers supercomputer. We show using simulation that a light-weight history-based prediction method can provide accurate enough power prediction to improve the energy management of a large scale supercomputer compared to energy-unaware scheduling algorithms. These improvements have no significant negative impact on performance.

Reducing greenhouse gas (GHG) emissions from Information and Communication Technology (ICT) has become a hot topic since the Paris Agreement. Data centers are one of the most impactful ICT energy consumers since they are built to run 24 hours / 7 days. An emerging discussion is switching their power supply from brown to green energy, using Renewable Energy Sources (RES). However, this change introduces uncertainties linked to production intermittence. This work is part of the Datazero2 project. This project designs a data center powered only by renewable production, adding storage elements to reduce the impact of the intermittence. A clean-by-design data center requires several decisions at different levels of management. To do so, it uses predictions to plan the actions for the next few days. However, it also needs to react to the actual events that can vary from the forecast. This work presents the BEASY heuristic. BEASY mixes power and scheduling decisions in a renewable-only data center, seeking to reduce the number of killed jobs and wasted energy. The results demonstrate that BEASY reduced wasted energy by up to 35.33% in critical cases. Considering the killed jobs, it also kills fewer jobs than the state of art algorithms in all executions.

International audience

Both academics and industry have engaged their efforts in reducing greenhouse gas (GHG) emissions of Information and Communications Technology (ICT). Data centers are one of the most electricity-expensive ICT actors due to their uninterrupted service. Reducing the usage of brown energy or migrating to green energy using renewable sources (RES) is a way to reduce these emissions. However, this migration is not straightforward due to intermittence from these sources. This work is part of Datazero 2 ANR project. This project aims to design a data center powered only by RES production and storage elements. This architecture requires several decisions at different levels of management. This project divides the problem into two groups: offline and online. On the offline side, it uses renewable and workload predictions to prepare an offline plan with a power envelope (power delivered to the servers) and hints on how to manage the storage (batteries and hydrogen). Given the offline plan, the article's contribution is how to deal with real and dynamic power constraints online while keeping the planned storage level at the end. So, this article proposes policies to modify the plan according to the changes in predictions. We evaluate these policies in a homogeneous and heterogeneous data center. The results demonstrate that our policies could approach the storage level and improve Quality of Service (QoS) independently of data center infrastructures.

The Internet of Things (IoT) encompasses both large-scale deployed physical infrastructures and software layers that enable intuitive and transparent creation of applications. This highly distributed, energy-greedy environment must ensure the quality of deployed services while taking into account the heterogeneity of capabilities and protocols as well as users and objects mobility. Deployment infrastructure has been redesigned to provide the necessary features, including paradigms such as software-defined networks and Fog computing. The purpose of this article is to study IoT services placement in a Fog architecture. We propose a model of the infrastructure and IoT applications as well as a placement strategy taking into account system's energy consumption and applications delay violations minimization with a Discrete Particles Swarm Optimization algorithm (DPSO). Simulations have been done with iFogSim simulator. Results have been compared with heuristics coming from the literature: Binary Partical Swarm optimization (BPSO), Dicothomous Module Mapping (DCT), CloudOnly, IoTFogOnly, IoTCloud (IC) and FogCloud (FC) placement approaches.

Data centers are considered nowadays as the factories of the digital age, being currently responsible for consuming more energy than the entire United Kingdom. On the other side, the global total capacity of renewable power increases continuously. The combination of these two factors calls for new approaches in designing data centers powered only by renewable energy sources. Our work focuses on task scheduling optimization under a power envelope, and on the way to handle power starvation, i.e. when the available power does not provide sufficient resources to execute a given workload. To do so we utilize the concept of task degradation through cross-correlation to find where to place the tasks in order to reduce the data center profit degradation. The results show that our algorithm could obtain more than 34% increase in profit when compared to algorithms from the literature, while fulfilling the power profile and resources constraints.

As the number of data centers increases, it is urgent to reduce their energy consumption. Although servers are becoming more energy-efficient, their idle consumption remains high, which is an issue as data centers are often over-provisioned. This work proposes a novel approach for building data centers with heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time, and design a scheduler that dynamically reconfigures the infrastructure, by migrating applications and switching machines on or off, so that the energy consumed by the data center is proportional to the load. Experiments evaluate the approach and show the energy savings achieved by our heterogeneous data center design and management while satisfying Quality of Service (QoS) constraints.

The increasing number of data centers raises serious concerns regarding their energy consumption. Although servers have become more energy-efficient over time, their idle consumption remains high, which is an issue as resources in data centers are often over-provisioned. This work proposes a novel approach for building data centers so that their energy consumption is proportional to load. A data center hence comprises heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time and design a scheduler that dynamically reconfigures the infrastructure to minimize its energy consumption according to current load and application requirements. Based on load forecasts, it takes reconfiguration decisions and performs actions such as migrating applications and switching machines on or off. The approach is evaluated considering a data center with heterogeneous resources, and the experiments show how to adjust the parameters of scheduling policies to save the most energy while satisfying Quality of Service (QoS) constraints.

The huge amount of energy consumed by data centers represents a limiting factor in their operation. Many of these infrastructures are over-provisioned, thus a significant portion of this energy is consumed by inactive servers staying powered on even if the load is low. Although servers have become more energy-efficient over time, their idle power consumption remains still high. To tackle this issue, we consider a data center with an heterogeneous infrastructure composed of different machine types - from low power processors to classical powerful servers - in order to enhance its energy proportionality. We develop a dynamic provisioning algorithm which takes into account the various characteristics of the architectures composing the infrastructure: their performance, energy consumption and on/off reactivity. Based on future load information, it makes intelligent decisions of resource reconfiguration that impact the infrastructure at multiple terms. Our algorithm is reactive to load evolutions and is able to respect a perfect Quality of Service (QoS) while being energy-efficient. We evaluate our original approach with profiling data from real hardware and the experiments show that our dynamic provisioning brings significant energy savings compared to classical data centers operation.

We propose a multi-scale modeling approach for complex software system architecture description. The multi-scale description may help to obtain meaningful granularities of these systems and to understand and master their complexity. This vision enables an architect designer to express constraints concerning different description levels, oriented to facilitate adaptability management. We define a correct-by-design approach that allows a given abstract architectural description to be refined into architecture models. We follow a progressive refinement process based on model transformations; it begins with a coarse-grain description and ends with a fine-grain description that specifies design details. The adaptability property management is performed through model transformation operations. The model transformation ensures the correctness of UML description, and the correctness of the modeled system. We experimented our approach with a use case that models a smart home system for the monitoring of elderly and disabled persons at home.

Methodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, are illustrated, analysed and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can: (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; (v) guarantee theoretical properties of a grammar, like its termination.

The subsystems that compose a HPC platform (e.g. CPU, memory, storage and network) are often designed and configured to deliver exceptional performance to a wide range of workloads. As a result, a large part of the power that these subsystems consume is dissipated as heat even when executing workloads that do not require maximum performance. Attempts to tackle this problem include technologies whereby operating systems and applications can reconfigure subsystems dynamically, such as by using DVFS for CPUs, LPI for network components, and variable disk spinning for HDDs. Most previous work has explored these technologies individually to optimise workload execution and reduce energy consumption. We propose a framework that performs on-line analysis of an HPC system in order to identify application execution patterns without a priori information of their workload. The framework takes advantage of reoccurring patterns to reconfigure multiple subsystems dynamically and reduce overall energy consumption. Performance evaluation was carried out on Grid'5000 considering both traditional HPC benchmarks and real-life applications.

Heterogeneous servers are becoming prevalent in many high-performance computing environments, including clusters and data enters. In this paper, we consider multi-objective scheduling for heterogeneous server systems to optimize simultaneously the application performance, energy consumption and thermal imbalance. First, a greedy online framework is presented to allow the scheduling decisions to be made based on any well-defined cost function. To tackle the possibly conflicting objectives, we propose a fuzzy-based priority approach for exploring the tradeoffs of two or more objectives at the same time. Moreover, we present a heuristic algorithm for the static placement of physical machines in order to reduce the maximum temperature at the server outlets. Extensive simulations based on an emerging class of high-density server system have demonstrated the effectiveness of our proposed approach and heuristics in optimizing multiple objectives while achieving better thermal balance.

Complex Event Processing (CEP) is a set of tools and techniques that can be used to obtain insights from high-volume, high-velocity continuous streams of events. CEP-based systems have been adopted in many situations that require prompt establishment of system diagnostics and execution of reaction plans, such as in monitoring of complex systems. This article describes the Query Analyzer and Manager (QAM) module, a first effort toward the development of a CEP as a Service (CEPaaS) system. This module is responsible for analyzing user-defined CEP queries and for managing their execution in distributed cloud-based environments. Using a language-agnostic internal query representation, QAM has a modular design that enables its adoption by virtually any CEP system.

This paper introduces the Caging track (Collaborative and Autonomic Green computing). Topics, program committee, selected papers are explained.

SUMMARY Nowadays, there is no doubt that energy consumption has become a limiting factor in the design and operation of high performance computing (HPC) systems. This is evidenced by the rise of efforts both from the academia and the industry to reduce the energy consumption of those systems. Unlike hardware solutions, software initiatives targeting HPC systems' energy consumption reduction despite their effectiveness are often limited for reasons including (1) the program specific nature of the solution proposed; (2) the need of deep understanding of the application task at hand; and (3) proposed solutions are often difficult to use by novices and/or are designed for single task environments. This paper proposes a three step blind system‐wide, application independent, fine‐grain, and easy to use (user friendly) methodology for improving energy performance of HPC systems. The methodology typically breaks into phase detection, phase characterization, and phase identification and system reconfiguration. And it is blind in the sense that it does not require any knowledge from users. It relies upon reconfigurable capabilities offered by the majority of HPC subsystems—including the processor, storage, memory, and communication subsystems—to reduce the overall energy consumption of the system (excluding network equipments) at runtime. We also present an implementation of our methodology through which we demonstrate its effectiveness via static analyses and experiments using benchmarks representative of HPC workloads. The memory becoming one of the most power hungry HPC system this paper also introduces and investigates the relevance of a potential power saving scheme which we refer to as memory size scaling. It is destined to scale the memory size for saving energy in a CPU frequency scaling like fashion. Copyright © 2014 John Wiley & Sons, Ltd.

The rising computing demands of scientific endeavors often require the creation and management of High Performance Computing (HPC) systems for running experiments and processing vast amounts of data. These HPC systems generally operate at peak performance, consuming a large quantity of electricity, even though their workload varies over time. Understanding the behavioral patterns (i.e., phases) of HPC systems during their use is key to adjust performance to resource demand and hence improve the energy efficiency. In this paper, we describe (i) a method to detect phases of an HPC system based on its workload, and (ii) a partial phase recognition technique that works cooperatively with on-the-fly dynamic management. We implement a prototype that guides the use of energy saving capabilities to demonstrate the benefits of our approach. Experimental results reveal the effectiveness of the phase detection method under real-life workload and benchmarks. A comparison with baseline unmanaged execution shows that the partial phase recognition technique saves up to 15% of energy with less than 1% performance degradation.

Modern high performance computing subsystems (HPC) – including processor, network, memory, and IO – are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems considering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads.

Distributed software environments are increasingly complex and difficult to manage, as they integrate various legacy software with specific management interfaces. Moreover, the fact that management tasks are performed by humans leads to many configuration errors and low reactivity. This is particularly true in medium or large-scale grid infrastructures. To address this issue, we developed Jade, a middleware for self-management of distributed software environments. In this paper, we report on our experiments in using Jade for the management of grid applications.