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

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Multiattribute Call Markets.

Multiattribute auctions support automated negotiation in settings where buyers and sellers have valuations for alternate configurations of a good, as defined by configuration attributes. Bidders express offers to buy or sell alternate configurations by specifying configuration-dependent reserve prices, and the auction determines both the traded goods and transaction prices based on these offers. While multiattribute auctions have been deployed in single-buyer procurement settings, the development of double-sided multiattribute auctions-allowing the free participation of both buyers and sellers-has received little attention from academia or industry. In this work I develop a multiattribute call market, a specific type of double auction in which bids accumulate over an extended period of time, before the auction determines trades based on the aggregate collection of bids. Building on a polynomial-time clearing algorithm, I contribute an efficient algorithm for information feedback. Supporting the implementation of market-based algorithms, information feedback support extends the range of settings for which multiattribute call markets achieve efficiency. Multiattribute auctions are only one of many auction variants introduced in recent years. The rapidly growing space of alternative auctions and trading scenarios calls for both a standardized language with which to specify auctions, as well as a computational test environment in which to evaluate alternate designs. I present a novel auction description language and deployment environment that supports the specification of a broad class of auctions, improving on prior approaches through a scripting language that employs both static parameter settings and rule-based behavior invocation. The market game platform, AB3D, can execute these auction scripts to implement multi-auction and multi-agent trading scenarios. The efficiency of multiattribute call markets depends crucially on the underlying valuations of participants. I analyze the expected performance limitations of multiattribute call markets, using existing analytical results where applicable. Addressing a lack of theoretical guidance in many natural settings, I introduce a computational metric on bidder valuations, and show a correlation between this metric and the expected efficiency of multiattribute call markets. As further validation, I integrate multiattribute markets into an existing supply chain simulation, demonstrating efficiency gains over a more conventional negotiation procedure.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60822/1/klochner_1.pd

Market-based coordination for domestic demand response in low-carbon electricity grids

Efforts towards a low carbon economy are challenging the electricity industry. On the supply-side, centralised carbon-intensive power plants are set to gradually decrease their contribution to the generation mix, whilst distributed renewable generation is to successively increase its share. On the demand-side, electricity use is expected to increase in the future due to the electrification of heating and transport. Moreover, the demand-side is to become more active allowing end-users to invest in generation and storage technologies, such as solar photovoltaics (PV) and home batteries. As a result, some network reinforcements might be needed and instrumentation at the users’ end is to be required, such as controllers and home energy management systems (HEMS). The electricity grid must balance supply and demand at all times in order to maintain technical constraints of frequency, voltage, and current; and this will become more challenging as a result of this transition. Failure to meet these constraints compromises the service and could damage the power grid assets and end-users’ appliances. Balancing generation, although responsive, is carbon-intensive and associated with inefficient asset utilisation, as these generators are mostly used during peak hours and sit idle the rest of the time. Furthermore, energy storage is a potential solution to assist the balancing problem in the presence of non-dispatchable low-carbon generators; however, it is substantially expensive to store energy in large amounts. Therefore, demand response (DR) has been envisioned as a complementary solution to increase the system’s resilience to weather-dependent, stochastic, and intermittent generation along with variable and temperature-correlated electric load. In the domestic setting, operational flexibility of some appliances, such as heaters and electric cars, can be coordinated amongst several households so as to help balance supply and demand, and reduce the need of balancing generators. Against this background, the electricity supply system requires new organisational paradigms that integrate DR effectively. Although some dynamic pricing schemes have been proposed to guide DR, such as time of use (ToU) and real-time pricing (RTP), it is still unclear how to control oscillatory massive responses (e.g., large fleet of electric cars simultaneously responding to a favourable price). Hence, this thesis proposes an alternative approach in which households proactively submit DR offers that express their preferences to their respective retailer in exchange for a discount. This research develops a computational model of domestic electricity use, and simulates appliances with operational flexibility in order to evaluate the effects and benefits of DR for both retailers and households. It provides a representation for this flexibility so that it can be integrated into specific DR offers. Retailers and households are modelled as computational agents. Furthermore, two market-based mechanisms are proposed to determine the allocation of DR offers. More specifically, a one-sided Vickrey-Clarke-Groves (VCG)-based mechanism and penalty schemes were designed for electricity retailers to coordinate their customers’ DR efforts so as to ameliorate the imbalance of their trading schedules. Similarly, a two-sided McAfee-based mechanism was designed to integrate DR offers into a multi-retailer setting in order to reduce zonal imbalances. A suitable method was developed to construct DR block offers that could be traded amongst retailers. Both mechanisms are dominant-strategy incentive-compatible and trade off a small amount of economic efficiency in order to maintain individual rationality, truthful reporting, weak budget balance and tractable computation. Moreover, privacy preserving is achieved by including computational agents from the independent system operator (ISO) as intermediaries between each retailer and its domestic customers, and amongst retailers. The theoretical properties of these mechanisms were proved using worst-case analysis, and their economic effects were evaluated in simulations based on data from a survey of UK household electricity use. In addition, forecasting methods were assessed on the end-users’ side in order to make better DR offers and avoid penalties. The results show that, under reasonable assumptions, the proposed coordination mechanisms achieve significant savings for both end-users and retailers, as they reduce the required amount of expensive balancing generation

Dagstuhl News January - December 2001

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Strategic algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 193-201).Classical algorithms from theoretical computer science arise time and again in practice. However,a practical situations typically do not fit precisely into the traditional theoretical models. Additional necessary components are, for example, uncertainty and economic incentives. Therefore, modem algorithm design is calling for more interdisciplinary approaches, as well as for deeper theoretical understanding, so that the algorithms can apply to more realistic settings and complex systems. Consider, for instance, the classical shortest path algorithm, which, given a graph with specified edge weights, seeks the path minimizing the total weight from a source to a destination. In practice, the edge weights are often uncertain and it is not even clear what we mean by shortest path anymore: is it the path that minimizes the expected weight? Or its variance, or some another metric? With a risk-averse objective function that takes into account both mean and standard deviation, we run into nonconvex optimization challenges that require new theory beyond classical shortest path algorithm design. Yet another shortest path application, routing of packets in the Internet, needs to further incorporate economic incentives to reflect the various business relationships among the Internet Service Providers that affect the choice of packet routes. Strategic Algorithms are algorithms that integrate optimization, uncertainty and economic modeling into algorithm design, with the goal of bringing about new theoretical developments and solving practical applications arising in complex computational-economic systems.(cont.) In short, this thesis contributes new algorithms and their underlying theory at the interface of optimization, uncertainty and economics. Although the interplay of these disciplines is present in various forms in our work, for the sake of presentation we have divided the material into three categories: 1. In Part I we investigate algorithms at the intersection of Optimization and Uncertainty. The key conceptual contribution in this part is discovering a novel connection between stochastic and nonconvex optimization. Traditional algorithm design has not taken into account the risk inherent in stochastic optimization problems. We consider natural objectives that incorporate risk, which tum out equivalent to certain nonconvex problems from the realm of continuous optimization. As a result, our work advances the state of art in both stochastic and in nonconvex optimization, presenting new complexity results and proposing general purpose efficient approximation algorithms, some of which have shown promising practical performance and have been implemented in a real traffic prediction and navigation system. 2. Part II proposes new algorithm and mechanism design at the intersection of Uncertainty and Economics. In Part I we postulate that the random variables in our models come from given distributions. However, determining those distributions or their parameters is a challenging and fundamental problem in itself. A tool from Economics that has recently gained momentum for measuring the probability distribution of a random variable is an information or prediction market. Such markets, most popularly known for predicting the outcomes of political elections or other events of interest, have shown remarkable accuracy in practice, though at the same time have left open the theoretical and strategic analysis of current implementations, as well as the need for new and improved designs which handle more complex outcome spaces (probability distribution functions) as opposed to binary or n-ary valued distributions. The contributions of this part include a unified strategic analysis of different prediction market designs that have been implemented in practice.(cont.) We also offer new market designs for handling exponentially large outcome spaces stemming from ranking or permutation-type outcomes, together with algorithmic and complexity analysis. 3. In Part III we consider the interplay of optimization and economics in the context of network routing. This part is motivated by the network of autonomous systems in the Internet where each portion of the network is controlled by an Internet service provider, namely by a self-interested economic agent. The business incentives do not exist merely in addition to the computer protocols governing the network. Although they are not currently integrated in those protocols and are decided largely via private contracting and negotiations, these economic considerations are a principal factor that determines how packets are routed. And vice versa, the demand and flow of network traffic fundamentally affect provider contracts and prices. The contributions of this part are the design and analysis of economic mechanisms for network routing. The mechanisms are based on first- and second-price auctions (the so-called Vickrey-Clarke-Groves, or VCG mechanisms). We first analyze the equilibria and prices resulting from these mechanisms. We then investigate the compatibility of the better understood VCG-mechanisms with the current inter-domain routing protocols, and we demonstrate the critical importance of correct modeling and how it affects the complexity and algorithms necessary to implement the economic mechanisms.by Evdokia Velinova Nikolova.Ph.D

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version