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17th IEEE International Symposium on Embedded multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Li, Huilin Nakamura, Hiroshi Takase, Hideki The University of Tokyo Tokyo Japan

ISBN: (纸本)9798331530471

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and delay-sensitive tasks, the use of multi-access edge computing (MEC) has been proposed. However, owing to the performance limitations of UTDs and limited network resources, optimizing the energy consumption of UTDs under the constraint of a certain delay condition remains a key area of research. Therefore, this paper presents a resource allocation optimization strategy for MEC environments. First, we formulated the optimization problem as a constrained nonlinear programming problem, with the aim of minimizing the total time delay and energy consumption of UTDs under a specific delay constraint. Second, we proposed a reinforcement learning algorithm that uses an actor network to predict the next action based on the current state and a critic network to evaluate the quality of the prediction. A stable convergence solution for the original optimization problem was obtained within a limited number of training episodes. Finally, the performance of the proposed algorithm was compared with that of other classical methods, which demonstrated that the proposed algorithm outperforms other methods. © 2024 IEEE.

关键词： constrained nonlinear programming deep reinforcement learning multi-access edge computing resource allocation

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Stochastic model-driven capacity planning framework for multi-access edge computing

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computing 2022年第12期104卷 2557-2579页

作者： Shojaee, Reza Yazdani, Nasser Univ Tehran Sch Elect & Comp Engn Tehran Iran

multi-access edge computing (MEC) offers cloud computing capabilities and IT services situated at the Radio access Network (RAN) in the mobile users' proximity. Applications could offload their computation-intensive tasks to the MEC servers. Consequently, MEC significantly diminishes the mean response time and job rejection probability compared to conventional Mobile Cloud computing (MCC). Cost-performance trade-off is one of the major concerns of the system designers. Low performance leads to the Service Level Agreement (SLA) violation and disappoints the service consumers. On the other hand, reaching high performance by augmenting the number of servers in the MEC and Cloud sides incur more infrastructure and other operational costs. In this paper, we formulate the mentioned cost-performance trade-off into an optimization problem. We demonstrate that the optimization problem is integer and non-linear. Moreover, we propose a capacity planning framework to determine the optimal number of servers in the MEC and Cloud sides, minimizing the Total Cost of Ownership (TCO) with SLA satisfaction. The proposed capacity planning framework gains from the simulated annealing algorithm to obtain a globally optimum solution. Furthermore, we deploy the stochastic performance model to measure mean response time and job rejection probability at each iteration. Numerical results reveal that the proposed framework determines the optimal solution within a reasonable time.

关键词： multi-access edge computing Capacity Planning Stochastic Modeling Performance Evaluation

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Offloading Image Recognition Processing for Care Robots to FPGA on multi-access edge computing 22

Offloading Image Recognition Processing for Care Robots to F...

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22nd International Conference on Field Programmable Technology (ICFPT)

作者： Mori, Hayato Okazaki, Eisuke Nagahashi, Gai Sato, Mikiko Ohkawa, Takeshi Sugaya, Midori Shibaura Inst Technol Tokyo Japan Tokai Univ Tokyo Japan Kumamoto Univ Kumamoto Japan

ISBN: (纸本)9798350359114

Due to an aging population, autonomous robots with functions such as detecting falls in elderly individuals, patrolling, and delivering requested items are expected to alleviate the labor burden at nursing care sites. It is challenging to equip robots with computational resources capable of processing these functions due to power constraints, and there is concern that offloading to the cloud will result in significant communication delays. Therefore, we aim to achieve low-latency image processing on a robot using an edge computing method called MEC (multi-access edge computing). This method involves offloading processing from an edge device to a highperformance computing resource via high-speed 5G wireless communication. We also aim to minimize power consumption and latency by leveraging FPGAs, which are expected to serve as promising computational resources for MEC due to their low power consumption and high-speed parallel processing capabilities. In this presentation, we will demonstrate the implementation of the OpenPose-based posture estimation process used for fall detection on an FPGA and its utilization as a computational resource for MEC.

关键词： Care Robots multi-access edge computing FPGA

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OPTIMIST: OPTIMised video content delivery chains over joint multi-access edge computing and 5G radio network infrasTructures 26

OPTIMIST: OPTIMised video content delivery chains over joint...

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26th Euromicro Conference on Digital System Design (DSD) / 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Xenakis, Dionysis Centofanti, Carlo Rinaldi, Claudia Marotta, Andrea Verikoukis, Christos Passas, Nikos Tennina, Stefano Cassioli, Dajana Univ Aquila DISIM Laquila Italy CNIT Consorzio Nazl Interuniv Telecomunicaz Parma Italy Iquadrat Informat Barcelona Spain Univ Athens Dept Informat & Telecommun Athens Greece Fogus Innovat Serv Kaisariani Greece WEST Aquila Srl Laquila Italy

ISBN: (纸本)9798350344196

The OPTIMIST project is positioned into one of the most challenging and societal impactful innovation paths, by contributing to the design and implementation of a multi-access edge computing-empowered service platform enabling the optimized delivery of the most popular type of traffic in the forthcoming 5G networks: mobile video. Advanced prediction tools (e.g. data analytics), MEC-enabled service provisioning, 5G networking, QoE-driven and model-based service optimization using stochastic modeling and algorithmic optimization, are major components toward a holistic system that combines analytical tools and close-to-market technologies. In addition, despite the considerable amount of valuable academic research conducted in the area of MEC- enabled service provisioning, the vast majority of obtained results remains hardly compatible (in terms of system models and implementation platforms) and are often limited to the scope of academic research with no envisaged market transfer plans. OPTIMIST fosters fruitful collaboration of academia and industry targeting at developing an innovative platform for fully personalized delivery of 5G mobile video content.

关键词： multi-access edge computing 5G networks Quality of Experience mobile video service provisioning

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A Comparative Analysis of Machine Learning Techniques for Enhanced Resource Management in multi-access edge computing 9

A Comparative Analysis of Machine Learning Techniques for En...

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9th IEEE World Forum on the Internet of Things (WF-IoT) - The Blue Planet - A Marriage of Sea and Space

作者： Vinhal, Lucas Moreira, Rodrigo Silva, Flavio de Oliveira Univ Fed Uberlandia Fac Comp FACOM Uberlandia Brazil Fed Univ Vicosa UFV Vicosa Brazil Univ Minho UMinho Dept Informat DI Braga Portugal

ISBN: (纸本)9798350311617;9798350311624

The Internet of Things (IoT) merges physical and virtual realms, whereas Mobile access edge computing (MEC) facilitates various IoT applications in this context. Machine Learning (ML) can predict the optimal amount of resources, optimizing resource usage in edge environments with limited resources. However, selecting an appropriate approach is challenging because of the available techniques and algorithms. This study offers an in-depth analysis of several algorithms applied in diverse contexts for predictive autoscaling edge IoT applications. Using open datasets, we comprehensively compared these algorithms' performance across multiple scenarios commonly encountered at the edge of the network. We assessed their efficacy in univariate/multivariate, one-step/multistep forecasting, and regression/classification tasks. Our findings indicate no one-size-fits-all solution because different algorithms are more suitable for distinct scenarios.

关键词： Machine Learning Deep Learning multi-access edge computing IoT Time Series Forecasting Predictive Autoscaling

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Graph Attention Network Reinforcement Learning Based Computation Offloading in multi-access edge computing 47

Graph Attention Network Reinforcement Learning Based Computa...

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47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Liu, Yuxuan Xia, Geming Chen, Jian Zhang, Danlei Natl Univ Def Technol Coll Comp Sci & Pchnol Changsha Peoples R China

ISBN: (纸本)9798350326970

edge computing has emerged as a popular paradigm for handling heterogeneous tasks to improve computing capacity and promote quality of service (QoS). multi-access edge computing can balance overall benefits while increasing the efficiency of edge ***,the design of computation offloading is challenging. This study provides a Graph Attention Network Reinforcement Learning (GATRL) framework to address the offloading challenge, which models devices and edge servers as digraphs and the state policy by graph properties to address the problem. The GATRL uses supervised learning and combines the GAT and DQN. We propose a method to identify the approximate solution to achieve fast convergence based on the data generated by Gaussian distribution relied on actual parameters. Compared to other methods, numerical results show that the proposed GATRL algorithm can achieve near-optimal performance and significantly reduce the off-load *** addition, graph neural networks can adapt to more variable network environments, thus rapidly changing the model structure and giving offloading solutions.

关键词： multi-access edge computing computation offloading graph attention network deep-Q-learning

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Partial Computation Offloading in Parked Vehicle-Assisted multi-access edge computing: A Game-Theoretic Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 10220-10225页

作者： Pham, Xuan-Qui Huynh-The, Thien Huh, Eui-Nam Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Dept IT Convergence Gumi 39177 South Korea Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea

Parked vehicle-assisted multi-access edge computing (PVMEC) is a paradigm that exploits the under-utilized resources of parked vehicles (PVs) to assist MEC servers for offloaded task execution. This article investigates a partial offloading strategy for multi-user PVMEC, where each mobile device (MD)'s task can be partially offloaded to the MEC server or a nearby PV. We formulate the system utility maximization problem with joint consideration of offloading decisions, offloading ratio, and resource allocation. Considering the complexity and privacy issues of centralized scheme as well as the inherent resource contention among MDs, we adopt game-theoretic approach to devise a low-complexity distributed offloading scheme, in which the offloading decision-making problem is modeled as an exact potential game, and the optimal offloading ratio and resource allocation are determined using a subgradient method. Finally, simulation results show that our proposed approach can significantly improve the system utility over traditional baselines.

关键词： Servers Task analysis Games Resource management Costs multi-access edge computing Heuristic algorithms Computation offloading multi-access edge computing resource allocation vehicular network

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Joint Task Offloading and Migration Scheme Based on Optimal System Benefit in Mobility-aware multi-access edge computing Network 9

Joint Task Offloading and Migration Scheme Based on Optimal ...

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9th IEEE Smart World Congress, SWC 2023

作者： Wang, Chunyang Feng, Chuanfen Sun, Jiande Shandong Normal University Jinan China

ISBN: (纸本)9798350319804

multi-access edge computing (MEC) is considered as an effective method to provide computing resources to nearby users. However, it is difficult to find a suitable offloading and migration scheme for computing task in high-speed movement scenario. Considering the impact of connection time between mobile user and MEC server on system performance, a MEC task processing model is constructed. A task offloading and migration scheme based on the optimal system benefit is designed by defining the system benefit function. A task offloading algorithm based on particle swarm optimization (PSO) is proposed in order to maximize the system benefit. Comprehensive consideration of communication resource, calculation resource, energy consumption, connection time, and amount of task, the best MEC servers are selected to provide services for users, and the optimal task offloaded and migration is also given. Simulation results demonstrate that the system benefit of the scheme proposed in this paper is up to 12% higher than that of its several existing schemes. © 2023 IEEE.

关键词： multi-access edge computing Particle swarm optimization System benefit Task migration Task offloading

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Intrusion Detection in IoT leveraged by multi-access edge computing using Machine Learning

Intrusion Detection in IoT leveraged by Multi-Access Edge Co...

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IEEE International Performance, computing, and Communications Conference (IPCCC)

作者： Li, Dongjin Majd, Nahid Ebrahimi Calif State Univ San Marcos Dept Comp Sci & Informat Syst San Marcos TX USA

ISBN: (纸本)9798350302936

An intrusion detection system is a technology built to monitor, detect, and prevent malicious activities and cyberattacks towards computer networks. The Internet of Things (IoT) is a system of devices connected to each other and to the Internet that facilitates communication between the IoT devices and the cloud. This rapidly growing technology calls for more accurate and efficient cyberattack detection techniques to ensure the security of IoT devices and systems. In this paper, we focus on using machine learning and deep learning algorithms along with feature selection methods to detect cyberattacks effectively at the edge of IoT network by leveraging multi-access edge computing. This study addresses the challenges of processing intrusion detection datasets, such as data imbalance and missing data, with a focus on UNSW-NB15 dataset. We use ANOVA and embedded feature selection techniques and apply various machine learning algorithms, consisting of Decision Tree (DT), Random Forest (RF), Light gradient-boosting machine (LightGBM), Artificial Neural Network (ANN), K-Nearest Neighbor (kNN), and Extreme Gradient Boost (XGB), on UNSW-NB15 dataset. Our experimental results indicate that our classifiers achieved higher accuracies and efficiencies comparing to state-of-the-art machine learning intrusion detection approaches and our LightGBM model is the most accurate and efficient one among all.

关键词： Intrusion detection multi-access edge computing Network security Feature Selection Machine Learning

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multi-access edge computing fundamentals, services, enablers and challenges: A complete survey

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2022年 199卷 103308-103308页

作者： Liang, Bin Gregory, Mark A. Li, Shuo RMIT Univ Melbourne Vic Australia

Traffic over mobile cellular networks has significantly increased over the past decade, and with the introduction of 5G there is a growing focus on throughput capacity, reliability, and low latency to meet the demands of new and innovative applications. multi-access edge computing (MEC) is being developed to achieve a series of challenges posed by the introduction of new applications and services that require ultra-low latency and high bandwidth. This article is a comprehensive survey of recent advances in MEC and provides a description of the MEC concept, framework, and capabilities. We also summarize a set of MEC technology enablers including Software Defined Networking, Network Function Virtualization, Information-Centric Networking, Service Function Chaining, Cloud-Radio access Networks, Fog-computing based Radio access Networks and Network Slicing. The MEC use cases and the open research challenges are presented.

关键词： multi-access edge computing Software Defined Networking Network Function Virtualization Information-Centric Networking Service Function Chaining Network Slicing Cloud-Radio access Network Fog-computing based Radio access Network

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