78 research outputs found

    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

    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

    Cooperative scheduling and load balancing techniques in fog and edge computing

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    Fog and Edge Computing are two models that reached maturity in the last decade. Today, they are two solid concepts and plenty of literature tried to develop them. Also corroborated by the development of technologies, like for example 5G, they can now be considered de facto standards when building low and ultra-low latency applications, privacy-oriented solutions, industry 4.0 and smart city infrastructures. The common trait of Fog and Edge computing environments regards their inherent distributed and heterogeneous nature where the multiple (Fog or Edge) nodes are able to interact with each other with the essential purpose of pre-processing data gathered by the uncountable number of sensors to which they are connected to, even by running significant ML models and relying upon specific processors (TPU). However, nodes are often placed in a geographic domain, like a smart city, and the dynamic of the traffic during the day may cause some nodes to be overwhelmed by requests while others instead may become completely idle. To achieve the optimal usage of the system and also to guarantee the best possible QoS across all the users connected to the Fog or Edge nodes, the need to design load balancing and scheduling algorithms arises. In particular, a reasonable solution is to enable nodes to cooperate. This capability represents the main objective of this thesis, which is the design of fully distributed algorithms and solutions whose purpose is the one of balancing the load across all the nodes, also by following, if possible, QoS requirements in terms of latency or imposing constraints in terms of power consumption when the nodes are powered by green energy sources. Unfortunately, when a central orchestrator is missing, a crucial element which makes the design of such algorithms difficult is that nodes need to know the state of the others in order to make the best possible scheduling decision. However, it is not possible to retrieve the state without introducing further latency during the service of the request. Furthermore, the retrieved information about the state is always old, and as a consequence, the decision is always relying on imprecise data. In this thesis, the problem is circumvented in two main ways. The first one considers randomised algorithms which avoid probing all of the neighbour nodes in favour of at maximum two nodes picked at random. This is proven to bring an exponential improvement in performance with respect to the probe of a single node. The second approach, instead, considers Reinforcement Learning as a technique for inferring the state of the other nodes thanks to the reward received by the agents when requests are forwarded. Moreover, the thesis will also focus on the energy aspect of the Edge devices. In particular, will be analysed a scenario of Green Edge Computing, where devices are powered only by Photovoltaic Panels and a scenario of mobile offloading targeting ML image inference applications. Lastly, a final glance will be given at a series of infrastructural studies, which will give the foundations for implementing the proposed algorithms on real devices, in particular, Single Board Computers (SBCs). There will be presented a structural scheme of a testbed of Raspberry Pi boards, and a fully-fledged framework called ``P2PFaaS'' which allows the implementation of load balancing and scheduling algorithms based on the Function-as-a-Service (FaaS) paradigm

    Design of VLC and heterogeneous RF/VLC systems for future generation networks: an algorithmic approach

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    Visible light communication (VLC) has attracted significant research interest within the last decade due in part to the vast amount of unused transmission bandwidth in the visible light spectrum. VLC is expected to be part of future generation networks. The heterogeneous integration of radio frequency (RF) and VLC systems has been envisioned as a promising solution to increase the capacity of wireless networks, especially in indoor environments. However, the promised advantages of VLC and heterogeneous RF/VLC systems cannot be realized without proper resource management algorithms that exploit the distinguishing characteristics between RF and VLC systems. Further, the problem of backhauling for VLC systems has received little attention. This dissertation’s first part focuses on designing and optimizing VLC and heterogeneous RF/VLC systems. Novel resource allocation algorithms that optimize the sum-rate and energy efficiency performances of VLC, hybrid, and aggregated RF/VLC systems while considering practical constraints like illumination requirements, inter-cell interference, quality-of-service requirements, and transmit power budgets are proposed. Moreover, a power line communicationbased backhaul solution for an indoor VLC system is developed, and a backhaul-aware resource allocation algorithm is proposed. These algorithms are developed by leveraging tools from fractional programming (i.e., Dinkelbach’s transform and quadratic transform), the multiplier adjustment method, matching theory, and multi-objective optimization. The latter part of this dissertation examines the adoption of emerging beyond 5G technologies, such as intelligent reflecting surfaces (IRSs) and reconfigurable intelligent surfaces (RISs), to overcome the limitations of VLC systems and boost their performance gains. Novel system models for IRSs-aided and RISs-aided VLC systems are proposed, and metaheuristic-based algorithms are developed to optimize the configurations of the IRSs/RISs and, consequently, the performance of VLC systems. Extensive simulations reveal that the proposed resource allocation schemes outperform the considered benchmarks and provide performance close to the optimal solution. Furthermore, the proposed system models achieve superior performance compared to benchmark system models

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    ClickINC: In-network Computing as a Service in Heterogeneous Programmable Data-center Networks

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    In-Network Computing (INC) has found many applications for performance boosts or cost reduction. However, given heterogeneous devices, diverse applications, and multi-path network typologies, it is cumbersome and error-prone for application developers to effectively utilize the available network resources and gain predictable benefits without impeding normal network functions. Previous work is oriented to network operators more than application developers. We develop ClickINC to streamline the INC programming and deployment using a unified and automated workflow. ClickINC provides INC developers a modular programming abstractions, without concerning to the states of the devices and the network topology. We describe the ClickINC framework, model, language, workflow, and corresponding algorithms. Experiments on both an emulator and a prototype system demonstrate its feasibility and benefits

    Understanding Quantum Technologies 2022

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    Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

    SparkFlow : towards high-performance data analytics for Spark-based genome analysis

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    The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatic data. However, this complicated process has become the system bottleneck, requiring an amalgamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., BaseRecalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and processor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper’s findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.Postprin

    Context-based security function orchestration for the network edge

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    Over the last few years the number of interconnected devices has increased dramatically, generating zettabytes of traffic each year. In order to cater to the requirements of end-users, operators have deployed network services to enhance their infrastructure. Nowadays, telecommunications service providers are making use of virtualised, flexible, and cost-effective network-wide services, under what is known as Network Function Virtualisation (NFV). Future network and application requirements necessitate services to be delivered at the edge of the network, in close proximity to end-users, which has the potential to reduce end-to-end latency and minimise the utilisation of the core infrastructure while providing flexible allocation of resources. One class of functionality that NFV facilitates is the rapid deployment of network security services. However, the urgency for assuring connectivity to an ever increasing number of devices as well as their resource-constrained nature, has led to neglecting security principles and best practices. These low-cost devices are often exploited for malicious purposes in targeting the network infrastructure, with recent volumetric Distributed Denial of Service (DDoS) attacks often surpassing 1 terabyte per second of network traffic. The work presented in this thesis aims to identify the unique requirements of security modules implemented as Virtual Network Functions (VNFs), and the associated challenges in providing management and orchestration of complex chains consisting of multiple VNFs The work presented here focuses on deployment, placement, and lifecycle management of microservice-based security VNFs in resource-constrained environments using contextual information on device behaviour. Furthermore, the thesis presents a formulation of the latency-optimal placement of service chains at the network edge, provides an optimal solution using Integer Linear Programming, and an associated near-optimal heuristic solution that is able to solve larger-size problems in reduced time, which can be used in conjunction with context-based security paradigms. The results of this work demonstrate that lightweight security VNFs can be tailored for, and hosted on, a variety of devices, including commodity resource-constrained systems found in edge networks. Furthermore, using a context-based implementation of the management and orchestration of lightweight services enables the deployment of real-world complex security service chains tailored towards the user’s performance demands from the network. Finally, the results of this work show that on-path placement of service chains reduces the end-to-end latency and minimise the number of service-level agreement violations, therefore enabling secure use of latency-critical networks
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