Expert and operator perspectives on barriers to energy efficiency in data centers

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market

Das unstetige Galerkinverfahren für Strömungen mit freier Oberfläche und im Grundwasserbereich in geophysikalischen Anwendungen

Free surface flows and subsurface flows appear in a broad range of geophysical applications and in many environmental settings situations arise which even require the coupling of free surface and subsurface flows. Many of these application scenarios are characterized by large domain sizes and long simulation times. Hence, they need considerable amounts of computational work to achieve accurate solutions and the use of efficient algorithms and high performance computing resources to obtain results within a reasonable time frame is mandatory. Discontinuous Galerkin methods are a class of numerical methods for solving differential equations that share characteristics with methods from the finite volume and finite element frameworks. They feature high approximation orders, offer a large degree of flexibility, and are well-suited for parallel computing. This thesis consists of eight articles and an extended summary that describe the application of discontinuous Galerkin methods to mathematical models including free surface and subsurface flow scenarios with a strong focus on computational aspects. It covers discretization and implementation aspects, the parallelization of the method, and discrete stability analysis of the coupled model.Für viele geophysikalische Anwendungen spielen Strömungen mit freier Oberfläche und im Grundwasserbereich oder sogar die Kopplung dieser beiden eine zentrale Rolle. Oftmals charakteristisch für diese Anwendungsszenarien sind große Rechengebiete und lange Simulationszeiten. Folglich ist das Berechnen akkurater Lösungen mit beträchtlichem Rechenaufwand verbunden und der Einsatz effizienter Lösungsverfahren sowie von Techniken des Hochleistungsrechnens obligatorisch, um Ergebnisse innerhalb eines annehmbaren Zeitrahmens zu erhalten. Unstetige Galerkinverfahren stellen eine Gruppe numerischer Verfahren zum Lösen von Differentialgleichungen dar, und kombinieren Eigenschaften von Methoden der Finiten Volumen- und Finiten Elementeverfahren. Sie ermöglichen hohe Approximationsordnungen, bieten einen hohen Grad an Flexibilität und sind für paralleles Rechnen gut geeignet. Diese Dissertation besteht aus acht Artikeln und einer erweiterten Zusammenfassung, in diesen die Anwendung unstetiger Galerkinverfahren auf mathematische Modelle inklusive solcher für Strömungen mit freier Oberfläche und im Grundwasserbereich beschrieben wird. Die behandelten Themen umfassen Diskretisierungs- und Implementierungsaspekte, die Parallelisierung der Methode sowie eine diskrete Stabilitätsanalyse des gekoppelten Modells

An implementation path for green information technology systems in the Ghanaian mining industry

The mining and extractive industry’s operations have significant harmful environmental consequences. Mining companies have started adopting green supply chain management (GSCM) practices which include green information technology systems (GITS) to help provide economic benefits while seeking minimal environmental damage. These mining organizations face significant hurdles related to introducing and implementing various GSCM practices which can address some of the environmental burdens. This study addresses this issue by adopting a GSCM practices framework and applying a novel decision support method that integrates grey numbers with DEMATEL and the NK model for evaluating and developing an implementation path model. Using a multiple case field study with input from managers of the Ghanaian gold mining industry, the adopted GSCM practices framework and methodology is applied. The results provide an evaluation and development path model to guide these organizations and managers for GSCM planning and investment decisions. The path results show that these organizations should first develop SSP (Strategic Supplier Partnership) with their suppliers for implementing GITS (Green Information Technology and Systems) and other GSCM practices. These results provide some exploratory insight and guidelines for managers and policy-makers who seek to integrate green initiatives. This study also sets the stage for further investigation of organizational greening in developing countries and the mining industry

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen

A reference model for integrated energy and power management of HPC systems

Optimizing a computer for highest performance dictates the efficient use of its limited resources. Computers as a whole are rather complex. Therefore, it is not sufficient to consider optimizing hardware and software components independently. Instead, a holistic view to manage the interactions of all components is essential to achieve system-wide efficiency. For High Performance Computing (HPC) systems, today, the major limiting resources are energy and power. The hardware mechanisms to measure and control energy and power are exposed to software. The software systems using these mechanisms range from firmware, operating system, system software to tools and applications. Efforts to improve energy and power efficiency of HPC systems and the infrastructure of HPC centers achieve perpetual advances. In isolation, these efforts are unable to cope with the rising energy and power demands of large scale systems. A systematic way to integrate multiple optimization strategies, which build on complementary, interacting hardware and software systems is missing. This work provides a reference model for integrated energy and power management of HPC systems: the Open Integrated Energy and Power (OIEP) reference model. The goal is to enable the implementation, setup, and maintenance of modular system-wide energy and power management solutions. The proposed model goes beyond current practices, which focus on individual HPC centers or implementations, in that it allows to universally describe any hierarchical energy and power management systems with a multitude of requirements. The model builds solid foundations to be understandable and verifiable, to guarantee stable interaction of hardware and software components, for a known and trusted chain of command. This work identifies the main building blocks of the OIEP reference model, describes their abstract setup, and shows concrete instances thereof. A principal aspect is how the individual components are connected, interface in a hierarchical manner and thus can optimize for the global policy, pursued as a computing center's operating strategy. In addition to the reference model itself, a method for applying the reference model is presented. This method is used to show the practicality of the reference model and its application. For future research in energy and power management of HPC systems, the OIEP reference model forms a cornerstone to realize --- plan, develop and integrate --- innovative energy and power management solutions. For HPC systems themselves, it supports to transparently manage current systems with their inherent complexity, it allows to integrate novel solutions into existing setups, and it enables to design new systems from scratch. In fact, the OIEP reference model represents a basis for holistic efficient optimization.Computer auf höchstmögliche Rechenleistung zu optimieren bedingt Effizienzmaximierung aller limitierenden Ressourcen. Computer sind komplexe Systeme. Deshalb ist es nicht ausreichend, Hardware und Software isoliert zu betrachten. Stattdessen ist eine Gesamtsicht des Systems notwendig, um die Interaktionen aller Einzelkomponenten zu organisieren und systemweite Optimierungen zu ermöglichen. Für Höchstleistungsrechner (HLR) ist die limitierende Ressource heute ihre Leistungsaufnahme und der resultierende Gesamtenergieverbrauch. In aktuellen HLR-Systemen sind Energie- und Leistungsaufnahme programmatisch auslesbar als auch direkt und indirekt steuerbar. Diese Mechanismen werden in diversen Softwarekomponenten von Firmware, Betriebssystem, Systemsoftware bis hin zu Werkzeugen und Anwendungen genutzt und stetig weiterentwickelt. Durch die Komplexität der interagierenden Systeme ist eine systematische Optimierung des Gesamtsystems nur schwer durchführbar, als auch nachvollziehbar. Ein methodisches Vorgehen zur Integration verschiedener Optimierungsansätze, die auf komplementäre, interagierende Hardware- und Softwaresysteme aufbauen, fehlt. Diese Arbeit beschreibt ein Referenzmodell für integriertes Energie- und Leistungsmanagement von HLR-Systemen, das „Open Integrated Energy and Power (OIEP)“ Referenzmodell. Das Ziel ist ein Referenzmodell, dass die Entwicklung von modularen, systemweiten energie- und leistungsoptimierenden Sofware-Verbunden ermöglicht und diese als allgemeines hierarchisches Managementsystem beschreibt. Dies hebt das Modell von bisherigen Ansätzen ab, welche sich auf Einzellösungen, spezifischen Software oder die Bedürfnisse einzelner Rechenzentren beschränken. Dazu beschreibt es Grundlagen für ein planbares und verifizierbares Gesamtsystem und erlaubt nachvollziehbares und sicheres Delegieren von Energie- und Leistungsmanagement an Untersysteme unter Aufrechterhaltung der Befehlskette. Die Arbeit liefert die Grundlagen des Referenzmodells. Hierbei werden die Einzelkomponenten der Software-Verbunde identifiziert, deren abstrakter Aufbau sowie konkrete Instanziierungen gezeigt. Spezielles Augenmerk liegt auf dem hierarchischen Aufbau und der resultierenden Interaktionen der Komponenten. Die allgemeine Beschreibung des Referenzmodells erlaubt den Entwurf von Systemarchitekturen, welche letztendlich die Effizienzmaximierung der Ressource Energie mit den gegebenen Mechanismen ganzheitlich umsetzen können. Hierfür wird ein Verfahren zur methodischen Anwendung des Referenzmodells beschrieben, welches die Modellierung beliebiger Energie- und Leistungsverwaltungssystemen ermöglicht. Für Forschung im Bereich des Energie- und Leistungsmanagement für HLR bildet das OIEP Referenzmodell Eckstein, um Planung, Entwicklung und Integration von innovativen Lösungen umzusetzen. Für die HLR-Systeme selbst unterstützt es nachvollziehbare Verwaltung der komplexen Systeme und bietet die Möglichkeit, neue Beschaffungen und Entwicklungen erfolgreich zu integrieren. Das OIEP Referenzmodell bietet somit ein Fundament für gesamtheitliche effiziente Systemoptimierung

O contributo dos testes de software para a sua conceção mais eficiente do ponto de vista energético

Dissertação de Mestrado em Cidadania Ambiental e Participação apresentada à Universidade AbertaA mudança climática acelerada e adulterada pela humanização do planeta já não pode ser desmentida. A humanidade nas duas últimas centenas de anos desenvolveu uma tecnologia a que apelidou de “eletricidade” desde então moldou toda a sua vivência em torno desta tecnologia. Porém para a produzir “eletricidade” recorre-se, em boa medida à combustão de materiais que libertam CO2 em quantidades que pelo seu volume degradam o meio ambiente. Por outro lado, nem toda a humanidade tem acesso à “eletricidade” de forma abundante pelo que o seu consumo em determinadas zonas geográficas é racionado, impossibilitando assim que mais seres humanos entrem num melhor estado de desenvolvimento social. Esta dupla situação é confrontada com o facto de as sociedades contemporâneas utilizarem cada vez mais sistemas informáticos numa multiplicidade de tarefas diárias. Neste contexto, surgiu uma nova profissão denominada como Software Testing que tem como grande objetivo a validação e verificação da qualidade do software produzido. A vertente de consumo energético do software é um tema quase inexistente na comunidades de testers, pelo que esta dissertação pretende a) documentar o status quo do tema, b) demonstrar um método simples de apurar o consumo do software e, por último, c) demonstrar, por diversos meios, entre eles via fórmula matemática, que a partir de determinado ponto (não é só do ponto de vista ambiental pela redução da emissão de CO2) os testes ao consumo energético do software se devem realizar mas porque há retorno financeiro direto e imediato (pela redução de consumo de energia).Climate change cannot be ignored any longer. Not only this is a natural phenomenon but also it has been speeded up owing to human activities. To support most of those activities Humanity has developed a technology called "electricity". In the vast of majority of cases the generation of “electricity” is linked to the combustion of products that release CO2 into the atmosphere. On the other hand, not all countries has access to "electricity" in the same way – in an abundant way. In areas where its consumption is rationed human social development is delayed or even impaired. This situation is even more dramatic now-a-days since there is a multitude of human tasks that are dependent on electricity such as the use of personal computers which are now part of our daily life. In this context, a new professional category emerges – the software testing, which main objective is to verify and validate the quality of the software that is produced. The aspect of energy consumption by the software is not yet seen as important theme in our profession and that is why the present work aims at a) enlighten about the present situation, b) to reveal a simple method to measure software energy consumption and c) demonstrate by different ways, that after a certain point, the energy consumption it is not only important because of the environment (and quantity of CO2 release into the atmosphere) but also because of economical expenditur

Operating policies for energy efficient large scale computing

PhD ThesisEnergy costs now dominate IT infrastructure total cost of ownership, with datacentre operators predicted to spend more on energy than hardware infrastructure in the next five years. With Western European datacentre power consumption estimated at 56 TWh/year in 2007 and projected to double by 2020, improvements in energy efficiency of IT operations is imperative. The issue is further compounded by social and political factors and strict environmental legislation governing organisations. One such example of large IT systems includes high-throughput cycle stealing distributed systems such as HTCondor and BOINC, which allow organisations to leverage spare capacity on existing infrastructure to undertake valuable computation. As a consequence of increased scrutiny of the energy impact of these systems, aggressive power management policies are often employed to reduce the energy impact of institutional clusters, but in doing so these policies severely restrict the computational resources available for high-throughput systems. These policies are often configured to quickly transition servers and end-user cluster machines into low power states after only short idle periods, further compounding the issue of reliability. In this thesis, we evaluate operating policies for energy efficiency in large-scale computing environments by means of trace-driven discrete event simulation, leveraging real-world workload traces collected within Newcastle University. The major contributions of this thesis are as follows: i) Evaluation of novel energy efficient management policies for a decentralised peer-to-peer (P2P) BitTorrent environment. ii) Introduce a novel simulation environment for the evaluation of energy efficiency of large scale high-throughput computing systems, and propose a generalisable model of energy consumption in high-throughput computing systems. iii iii) Proposal and evaluation of resource allocation strategies for energy consumption in high-throughput computing systems for a real workload. iv) Proposal and evaluation for a realworkload ofmechanisms to reduce wasted task execution within high-throughput computing systems to reduce energy consumption. v) Evaluation of the impact of fault tolerance mechanisms on energy consumption