722 research outputs found

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Blending the Material and Digital World for Hybrid Interfaces

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    The development of digital technologies in the 21st century is progressing continuously and new device classes such as tablets, smartphones or smartwatches are finding their way into our everyday lives. However, this development also poses problems, as these prevailing touch and gestural interfaces often lack tangibility, take little account of haptic qualities and therefore require full attention from their users. Compared to traditional tools and analog interfaces, the human skills to experience and manipulate material in its natural environment and context remain unexploited. To combine the best of both, a key question is how it is possible to blend the material world and digital world to design and realize novel hybrid interfaces in a meaningful way. Research on Tangible User Interfaces (TUIs) investigates the coupling between physical objects and virtual data. In contrast, hybrid interfaces, which specifically aim to digitally enrich analog artifacts of everyday work, have not yet been sufficiently researched and systematically discussed. Therefore, this doctoral thesis rethinks how user interfaces can provide useful digital functionality while maintaining their physical properties and familiar patterns of use in the real world. However, the development of such hybrid interfaces raises overarching research questions about the design: Which kind of physical interfaces are worth exploring? What type of digital enhancement will improve existing interfaces? How can hybrid interfaces retain their physical properties while enabling new digital functions? What are suitable methods to explore different design? And how to support technology-enthusiast users in prototyping? For a systematic investigation, the thesis builds on a design-oriented, exploratory and iterative development process using digital fabrication methods and novel materials. As a main contribution, four specific research projects are presented that apply and discuss different visual and interactive augmentation principles along real-world applications. The applications range from digitally-enhanced paper, interactive cords over visual watch strap extensions to novel prototyping tools for smart garments. While almost all of them integrate visual feedback and haptic input, none of them are built on rigid, rectangular pixel screens or use standard input modalities, as they all aim to reveal new design approaches. The dissertation shows how valuable it can be to rethink familiar, analog applications while thoughtfully extending them digitally. Finally, this thesis’ extensive work of engineering versatile research platforms is accompanied by overarching conceptual work, user evaluations and technical experiments, as well as literature reviews.Die Durchdringung digitaler Technologien im 21. Jahrhundert schreitet stetig voran und neue GerĂ€teklassen wie Tablets, Smartphones oder Smartwatches erobern unseren Alltag. Diese Entwicklung birgt aber auch Probleme, denn die vorherrschenden berĂŒhrungsempfindlichen OberflĂ€chen berĂŒcksichtigen kaum haptische QualitĂ€ten und erfordern daher die volle Aufmerksamkeit ihrer Nutzer:innen. Im Vergleich zu traditionellen Werkzeugen und analogen Schnittstellen bleiben die menschlichen FĂ€higkeiten ungenutzt, die Umwelt mit allen Sinnen zu begreifen und wahrzunehmen. Um das Beste aus beiden Welten zu vereinen, stellt sich daher die Frage, wie neuartige hybride Schnittstellen sinnvoll gestaltet und realisiert werden können, um die materielle und die digitale Welt zu verschmelzen. In der Forschung zu Tangible User Interfaces (TUIs) wird die Verbindung zwischen physischen Objekten und virtuellen Daten untersucht. Noch nicht ausreichend erforscht wurden hingegen hybride Schnittstellen, die speziell darauf abzielen, physische GegenstĂ€nde des Alltags digital zu erweitern und anhand geeigneter Designparameter und EntwurfsrĂ€ume systematisch zu untersuchen. In dieser Dissertation wird daher untersucht, wie MaterialitĂ€t und DigitalitĂ€t nahtlos ineinander ĂŒbergehen können. Es soll erforscht werden, wie kĂŒnftige Benutzungsschnittstellen nĂŒtzliche digitale Funktionen bereitstellen können, ohne ihre physischen Eigenschaften und vertrauten Nutzungsmuster in der realen Welt zu verlieren. Die Entwicklung solcher hybriden AnsĂ€tze wirft jedoch ĂŒbergreifende Forschungsfragen zum Design auf: Welche Arten von physischen Schnittstellen sind es wert, betrachtet zu werden? Welche Art von digitaler Erweiterung verbessert das Bestehende? Wie können hybride Konzepte ihre physischen Eigenschaften beibehalten und gleichzeitig neue digitale Funktionen ermöglichen? Was sind geeignete Methoden, um verschiedene Designs zu erforschen? Wie kann man Technologiebegeisterte bei der Erstellung von Prototypen unterstĂŒtzen? FĂŒr eine systematische Untersuchung stĂŒtzt sich die Arbeit auf einen designorientierten, explorativen und iterativen Entwicklungsprozess unter Verwendung digitaler Fabrikationsmethoden und neuartiger Materialien. Im Hauptteil werden vier Forschungsprojekte vorgestellt, die verschiedene visuelle und interaktive Prinzipien entlang realer Anwendungen diskutieren. Die Szenarien reichen von digital angereichertem Papier, interaktiven Kordeln ĂŒber visuelle Erweiterungen von UhrarmbĂ€ndern bis hin zu neuartigen Prototyping-Tools fĂŒr intelligente KleidungsstĂŒcke. Um neue DesignansĂ€tze aufzuzeigen, integrieren nahezu alle visuelles Feedback und haptische Eingaben, um Alternativen zu Standard-EingabemodalitĂ€ten auf starren Pixelbildschirmen zu schaffen. Die Dissertation hat gezeigt, wie wertvoll es sein kann, bekannte, analoge Anwendungen zu ĂŒberdenken und sie dabei gleichzeitig mit Bedacht digital zu erweitern. Dabei umfasst die vorliegende Arbeit sowohl realisierte technische Forschungsplattformen als auch ĂŒbergreifende konzeptionelle Arbeiten, Nutzerstudien und technische Experimente sowie die Analyse existierender Forschungsarbeiten

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark

    QoS-aware architectures, technologies, and middleware for the cloud continuum

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    The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions

    Increasing the Reliability of Power and Communication Networks via Robust Optimization

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    Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability. Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms. This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints. Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures. We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation

    Improving low latency applications for reconfigurable devices

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    This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed. Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods. Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture. Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces
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