Chapter One – An Overview of Architecture-Level Power- and Energy-Efficient Design Techniques

Power dissipation and energy consumption became the primary design constraint for almost all computer systems in the last 15 years. Both computer architects and circuit designers intent to reduce power and energy (without a performance degradation) at all design levels, as it is currently the main obstacle to continue with further scaling according to Moore's law. The aim of this survey is to provide a comprehensive overview of power- and energy-efficient “state-of-the-art” techniques. We classify techniques by component where they apply to, which is the most natural way from a designer point of view. We further divide the techniques by the component of power/energy they optimize (static or dynamic), covering in that way complete low-power design flow at the architectural level. At the end, we conclude that only a holistic approach that assumes optimizations at all design levels can lead to significant savings.Peer ReviewedPostprint (published version

Resource Management for Edge Computing in Internet of Things (IoT)

Die große Anzahl an Geräten im Internet der Dinge (IoT) und deren kontinuierliche Datensammlungen führen zu einem rapiden Wachstum der gesammelten Datenmenge. Die Daten komplett mittels zentraler Cloud Server zu verarbeiten ist ineffizient und zum Teil sogar unmöglich oder unnötig. Darum wird die Datenverarbeitung an den Rand des Netzwerks verschoben, was zu den Konzepten des Edge Computings geführt hat. Informationsverarbeitung nahe an der Datenquelle (z.B. auf Gateways und Edge Geräten) reduziert nicht nur die hohe Arbeitslast zentraler Server und Netzwerke, sondern verringer auch die Latenz für Echtzeitanwendungen, da die potentiell unzuverlässige Kommunikation zu Cloud Servern mit ihrer unvorhersehbaren Netzwerklatenz vermieden wird. Aktuelle IoT Architekturen verwenden Gateways, um anwendungsspezifische Verbindungen zu IoT Geräten herzustellen. In typischen Konfigurationen teilen sich mehrere IoT Edge Geräte ein IoT Gateway. Wegen der begrenzten verfügbaren Bandbreite und Rechenkapazität eines IoT Gateways muss die Servicequalität (SQ) der verbundenen IoT Edge Geräte über die Zeit angepasst werden. Nicht nur um die Anforderungen der einzelnen Nutzer der IoT Geräte zu erfüllen, sondern auch um die SQBedürfnisse der anderen IoT Edge Geräte desselben Gateways zu tolerieren. Diese Arbeit untersucht zuerst essentielle Technologien für IoT und existierende Trends. Dabei werden charakteristische Eigenschaften von IoT für die Embedded Domäne, sowie eine umfassende IoT Perspektive für Eingebettete Systeme vorgestellt. Mehrere Anwendungen aus dem Gesundheitsbereich werden untersucht und implementiert, um ein Model für deren Datenverarbeitungssoftware abzuleiten. Dieses Anwendungsmodell hilft bei der Identifikation verschiedener Betriebsmodi. IoT Systeme erwarten von den Edge Geräten, dass sie mehrere Betriebsmodi unterstützen, um sich während des Betriebs an wechselnde Szenarien anpassen zu können. Z.B. Energiesparmodi bei geringen Batteriereserven trotz gleichzeitiger Aufrechterhaltung der kritischen Funktionalität oder einen Modus, um die Servicequalität auf Wunsch des Nutzers zu erhöhen etc. Diese Modi verwenden entweder verschiedene Auslagerungsschemata (z.B. die übertragung von Rohdaten, von partiell bearbeiteten Daten, oder nur des finalen Ergebnisses) oder verschiedene Servicequalitäten. Betriebsmodi unterscheiden sich in ihren Ressourcenanforderungen sowohl auf dem Gerät (z.B. Energieverbrauch), wie auch auf dem Gateway (z.B. Kommunikationsbandbreite, Rechenleistung, Speicher etc.). Die Auswahl des besten Betriebsmodus für Edge Geräte ist eine Herausforderung in Anbetracht der begrenzten Ressourcen am Rand des Netzwerks (z.B. Bandbreite und Rechenleistung des gemeinsamen Gateways), diverser Randbedingungen der IoT Edge Geräte (z.B. Batterielaufzeit, Servicequalität etc.) und der Laufzeitvariabilität am Rand der IoT Infrastruktur. In dieser Arbeit werden schnelle und effiziente Auswahltechniken für Betriebsmodi entwickelt und präsentiert. Wenn sich IoT Geräte in der Reichweite mehrerer Gateways befinden, ist die Verwaltung der gemeinsamen Ressourcen und die Auswahl der Betriebsmodi für die IoT Geräte sogar noch komplexer. In dieser Arbeit wird ein verteilter handelsorientierter Geräteverwaltungsmechanismus für IoT Systeme mit mehreren Gateways präsentiert. Dieser Mechanismus zielt auf das kombinierte Problem des Bindens (d.h. ein Gateway für jedes IoT Gerät bestimmen) und der Allokation (d.h. die zugewiesenen Ressourcen für jedes Gerät bestimmen) ab. Beginnend mit einer initialen Konfiguration verhandeln und kommunizieren die Gateways miteinander und migrieren IoT Geräte zwischen den Gateways, wenn es den Nutzen für das Gesamtsystem erhöht. In dieser Arbeit werden auch anwendungsspezifische Optimierungen für IoT Geräte vorgestellt. Drei Anwendungen für den Gesundheitsbereich wurden realisiert und für tragbare IoT Geräte untersucht. Es wird auch eine neuartige Kompressionsmethode vorgestellt, die speziell für IoT Anwendungen geeignet ist, die Bio-Signale für Gesundheitsüberwachungen verarbeiten. Diese Technik reduziert die zu übertragende Datenmenge des IoT Gerätes, wodurch die Ressourcenauslastung auf dem Gerät und dem gemeinsamen Gateway reduziert wird. Um die vorgeschlagenen Techniken und Mechanismen zu evaluieren, wurden einige Anwendungen auf IoT Plattformen untersucht, um ihre Parameter, wie die Ausführungszeit und Ressourcennutzung, zu bestimmen. Diese Parameter wurden dann in einem Rahmenwerk verwendet, welches das IoT Netzwerk modelliert, die Interaktion zwischen Geräten und Gateway erfasst und den Kommunikationsoverhead sowie die erreichte Batterielebenszeit und Servicequalität der Geräte misst. Die Algorithmen zur Auswahl der Betriebsmodi wurden zusätzlich auf IoT Plattformen implementiert, um ihre Overheads bzgl. Ausführungszeit und Speicherverbrauch zu messen

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

Personalizing education with algorithmic course selection

The work presented in this thesis utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, topics or modules) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The original research contribution of this thesis is an algorithm that can generate a schedule of courses with consideration of a student\u27s profile, minimization of cost, and complete adherence to institution requirements. The research problem at hand - a constrained optimization problem with potentially conflicting objectives - is solved by first identifying a minimal sets of courses a student can take to graduate and then intelligently placing the selected courses into available semesters. The distinction between the proposed approach and related studies is in its simultaneous achievement of the following: guaranteed fulfillment of curricular requirements; applicability to both traditional and non-traditional curricula; and flexibility in nomenclature - semantics are extracted from syntax to allow the identification of relevant content, despite differences in course or topic titles from one institution to the next. The course selection algorithm presented is developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with the assurance that recommended selections are always valid. With this algorithm, PERCEPOLIS can recommend the entire trajectory that a student could take to graduation, as opposed to just the next semester, and it does so with consideration of course or topic availability --Abstract, page iii

Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version

Hierarchical structure discovery and transfer in sequential decision problems

Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first contribution is the introduction of two frameworks for the transfer of hierarchical structure in sequential decision problems. The MASH framework facilitates transfer among multiple agents coordinating within a domain. The VRHRL framework allows an agent to transfer its knowledge across a family of domains that share the same transition dynamics but have differing reward dynamics. Both MASH and VRHRL are validated empirically in large domains and the results demonstrate significant speedup in the solutions due to transfer. The second contribution is a new approach to the discovery of hierarchical structure in sequential decision problems. HI-MAT leverages action models to analyze the relevant dependencies in a hierarchically-generated trajectory and it discovers hierarchical structure that transfers to all problems whose actions share the same relevant dependencies as the single source problem. HierGen advances HI-MAT by learning simple action models, leveraging these models to analyze non-hierarchically-generated trajectories from multiple source problems in a robust causal fashion, and discovering hierarchical structure that transfers to all problems whose actions share the same causal dependencies as those in the source problems. Empirical evaluations in multiple domains demonstrate that the discovered hierarchical structures are comparable to manually-designed structures in quality and performance. Action models are essential to hierarchical structure discovery and other aspects of intelligent behavior. The third contribution of this dissertation is the introduction of two general frameworks for learning action models in sequential decision problems. In the MBP framework, learning is user-driven; in the PLEX framework, the learner generates its own problems. The frameworks are formally analyzed and reduced to concept learning with one-sided error. A general action-modeling language is shown to be efficiently learnable in both frameworks

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe