1,182 research outputs found

    Performance Analytical Modelling of Mobile Edge Computing for Mobile Vehicular Applications: A Worst-Case Perspective

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    Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is firstly established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems

    Distributed Digital Twin Migration in Multi-tier Computing Systems

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    At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities. Deploying digital twins in the multi-tier computing system helps to realize ultra-reliable and low-latency interactions between users and their virtual objects. Considering users in the system may roam between edge servers with limited coverage and increase the data synchronization latency to their digital twins, it is crucial to address the digital twin migration problem to enable real-time synchronization between digital twins and users. To this end, we formulate a joint digital twin migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered. By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods. For the digital twin migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we propose a novel agent-contribution-enabled multi-agent reinforcement learning (AC-MARL) algorithm to enable distributed digital twin migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents. In addition, we utilize embedding matrices to code agents' actions and states to release the scalability issue under the high dimensional state in AC-MARL. Simulation results based on two real-world taxi mobility trace datasets show that the proposed digital twin migration scheme is able to reduce 23%-30% data synchronization latency for users compared to the benchmark schemes

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

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    The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

    Estudo de impacto de técnicas de IA e comunicação para aplicações edge

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    O seguinte trabalho apresenta uma avaliação do impacto da tecnologia 5G nas aplicações de reconhecimento de imagens em tempo real tomando por caso concreto o algoritmo You Only Look Once (YOLO) (REDMON; FARHADI, 2016a). É construída uma aplicação em nuvem de forma a verificar os impactos da tecnologia 5G no provimento de visão computacional. Tendo sido realizado no contexto da pandemia, o trabalho buscou aplicar o algoritmo YOLO na solução do problema real de reconhecimento e extração de dados documentais no contexto de atendimento remoto. Essa necessidade vem de encontro aos novos mode los de cadastro biométrico e documental de vários órgãos governamentais como Tribunal Superior Eleitoral, Auxílio Brasil, Portal Gov.Br entre outros. Para isso foi construída uma aplicação Python capaz de rodar em conteineres da plata forma Cuda executados em Graphical Processing Units NVIDIA. A aplicação tem como entrada a imagem de um documento e como saída o conjunto de informações nele con tido. O projeto prevê grande escalabilidade capaz de atender as demandas de uma solução digital para a totalidade da sociedade brasileira. Neste trabalho focou-se na criação de um serviço de reconhecimento e validação de documentos através de visão computacional, tomando como técnica fundamental o algoritmo You Only Look Once (YOLO). Tal escolha foi baseada na versatilidade e alta perfor mance apresentada pelo algoritmo, a qual se mostrou ideal para a classe de problema encontrado no atendimento dessa demanda. O resultado final deste estudo foi o viabili zador da solução Cognitive Document Validation(CDV) do sistema Datavalid disponível em: https://www.loja.serpro.gov.br/datavalid. Esse cenário foi motivado por demandas excepcionais como a pandemia de Covid-19 e a migração das bases de dados documentais dos registros físicos para a núvem. Surge nesse contexto a necessidade da verificação dos dados documentais em grande velocidade para atender a população, essa verificação precisa ser oferecida de maneira transparente para uma série de dispotivos que acessam aplicativos como o auxílio brasil e sistemas de embarque de companias aéreas. Tem-se como resultado final a elaboração de uma arqui tetura de serviço implementada como um sistema publicamente disponível no barramento de aplicações do Serviço Federal de Processamento de Dados (SERPRO).The following work presents an evaluation of the alternatives for object recognition in videos through the YOLO (You Only Look Once) algorithm (REDMON; FARHADI, 2016a). Cloud computing, mobile and local processing approaches are explored on dif ferent platforms. Being made in the pandemic context this works aims to apply the YOLO algorithm into a real world case study to recognize and extract documental data in the remote office sce nario. This needs comex towards the new models of biometric registration and documen tal validation of many governmental offices as the Tribunal Superior Eleitoral, Auxílio Brasil, Portal Gov.Br among others. Starting from a implementation of the Darknet Neural Network running the YOLO algo rithm until transform it into and API avaliable on the cloud

    Multi-objective resource optimization in space-aerial-ground-sea integrated networks

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    Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground, and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including resource optimization when the users have diverse requirements and applications. We present a comprehensive review of SAGSI networks from a resource optimization perspective. We discuss use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization, network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of network and performance degradation, software-defined networking, and intelligent surveillance and relay communication. We then formulate a mathematical framework for maximizing energy efficiency, resource utilization, and user association. We optimize user association while satisfying the constraints of transmit power, data rate, and user association with priority. The binary decision variable is used to associate users with system resources. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. Based on our formulated framework, we simulate and analyze the performance of three different algorithms (branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare the results. Simulation results show that the branch and bound algorithm shows the best results, so this is our benchmark algorithm. The complexity of branch and bound increases exponentially as the number of users and stations increases in the SAGSI network. We got comparable results for the interior point method and barrier simplex algorithm to the benchmark algorithm with low complexity. Finally, we discuss future research directions and challenges of resource optimization in SAGSI networks

    A survey on reconfigurable intelligent surfaces: wireless communication perspective

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    Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
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