48 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

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Accelerating Audio Data Analysis with In-Network Computing

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    Digital transformation will experience massive connections and massive data handling. This will imply a growing demand for computing in communication networks due to network softwarization. Moreover, digital transformation will host very sensitive verticals, requiring high end-to-end reliability and low latency. Accordingly, the emerging concept “in-network computing” has been arising. This means integrating the network communications with computing and also performing computations on the transport path of the network. This can be used to deliver actionable information directly to end users instead of raw data. However, this change of paradigm to in-network computing raises disruptive challenges to the current communication networks. In-network computing (i) expects the network to host general-purpose softwarized network functions and (ii) encourages the packet payload to be modified. Yet, today’s networks are designed to focus on packet forwarding functions, and packet payloads should not be touched in the forwarding path, under the current end-to-end transport mechanisms. This dissertation presents fullstack in-network computing solutions, jointly designed from network and computing perspectives to accelerate data analysis applications, specifically for acoustic data analysis. In the computing domain, two design paradigms of computational logic, namely progressive computing and traffic filtering, are proposed in this dissertation for data reconstruction and feature extraction tasks. Two widely used practical use cases, Blind Source Separation (BSS) and anomaly detection, are selected to demonstrate the design of computing modules for data reconstruction and feature extraction tasks in the in-network computing scheme, respectively. Following these two design paradigms of progressive computing and traffic filtering, this dissertation designs two computing modules: progressive ICA (pICA) and You only hear once (Yoho) for BSS and anomaly detection, respectively. These lightweight computing modules can cooperatively perform computational tasks along the forwarding path. In this way, computational virtual functions can be introduced into the network, addressing the first challenge mentioned above, namely that the network should be able to host general-purpose softwarized network functions. In this dissertation, quantitative simulations have shown that the computing time of pICA and Yoho in in-network computing scenarios is significantly reduced, since pICA and Yoho are performed, simultaneously with the data forwarding. At the same time, pICA guarantees the same computing accuracy, and Yoho’s computing accuracy is improved. Furthermore, this dissertation proposes a stateful transport module in the network domain to support in-network computing under the end-to-end transport architecture. The stateful transport module extends the IP packet header, so that network packets carry message-related metadata (message-based packaging). Additionally, the forwarding layer of the network device is optimized to be able to process the packet payload based on the computational state (state-based transport component). The second challenge posed by in-network computing has been tackled by supporting the modification of packet payloads. The two computational modules mentioned above and the stateful transport module form the designed in-network computing solutions. By merging pICA and Yoho with the stateful transport module, respectively, two emulation systems, i.e., in-network pICA and in-network Yoho, have been implemented in the Communication Networks Emulator (ComNetsEmu). Through quantitative emulations, the experimental results showed that in-network pICA accelerates the overall service time of BSS by up to 32.18%. On the other hand, using in-network Yoho accelerates the overall service time of anomaly detection by a maximum of 30.51%. These are promising results for the design and actual realization of future communication networks

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

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    Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Open Source Law, Policy and Practice

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    This book examines various policies, including the legal and commercial aspects of the Open Source phenomenon. Here, ‘Open Source’ is adopted as convenient shorthand for a collection of diverse users and communities, whose differences can be as great as their similarities. The common thread is their reliance on, and use of, law and legal mechanisms to govern the source code they write, use, and distribute. The central fact of open source is that maintaining control over source code relies on the existence and efficacy of intellectual property (‘IP’) laws, particularly copyright law. Copyright law is the primary statutory tool that achieves the end of openness, although implemented through private law arrangements at varying points within the software supply chain. This dependent relationship is itself a cause of concern for some philosophically in favour of ‘open’, with some predicting (or hoping) that the free software movement will bring about the end of copyright as a means for protecting software
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