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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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Efficient Microservice Deployment with Dependencies in multi-access edge computing 29

Efficient Microservice Deployment with Dependencies in Multi...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lu, Shuaibing Yan, Ran Wu, Jie Beijing University of Technology Faculty of Information Technology Beijing China Temple University Center for Networked Computing Philadelphia United States

ISBN: (纸本)9798350330717

In the context of mobile edge computing, efficiently deploying microservices to reduce finish time and enhance user service quality is a challenging task. However, existing research still has certain deficiencies in considering microservice deployment within edge server clusters and communication link constraints. To address this issue, we propose three microservice deployment strategies by offering flexibility and adaptability for various application scenarios. We initially consider two straightforward scenarios: one with unlimited storage resources under the bandwidth constraint, and the other with unlimited bandwidth resources under the storage constraint. For each of these two scenarios, we introduce a novel enhanced graph construction method and design two optimal solutions. For Scenario 3, which involves complex constraints on server capacity, computational capability, and communication resources, we present an optimization method based on main path partitioning and the simulated annealing algorithm. We effectively tackle challenges arising from server capacity, computational capability, and communication resource limitations. Across multiple experimental setups, our approach significantly improves microservice deployment efficiency and overall performance compared to traditional strategies. © 2023 IEEE.

关键词： dependency high-efficient microservice deployment multi-access edge computing

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Implementing zero trust security with dual fuzzy methodology for trust-aware authentication and task offloading in multi-access edge computing

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COMPUTER NETWORKS 2024年 241卷

作者： Ali, Belal Gregory, Mark A. Li, Shuo Dib, Omar Amjad RMIT Univ Sch Engn Melbourne Vic Australia KCST Univ Sch Engn Doha Kuwait

This paper proposes an efficient trust -aware authentication and task offloading scheme for multi -access edge computing (MEC) using the Zero Trust Security (ZTS) principles. The proposed method uses a dual fuzzy logic system to evaluate the trustworthiness of edge servers. Devices connected to the edge servers are authenticated using identity, biometrics and Physical Unclonable Function (PUF) measures. After authentication, tasks can be offloaded from the devices to the most trustworthy edge server. The proposed scheme also considers the resource constraints of the edge servers and aims to minimise the overall task completion time. The experimental results show that the proposed scheme outperforms existing schemes regarding authentication accuracy, task completion time, and energy consumption.

关键词： multi-access edge computing Security Task offloading Zero trust Trust-aware

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Design and Implementation of Endogenous Intelligence-based multi-access edge computing 33

Design and Implementation of Endogenous Intelligence-based M...

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33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Wang, Yuyang Zhao, Liqiang Tu, Haiyan Zhou, Guorong Yin, Qingyu Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China Huawei Technol Co Ltd Xian Res Ctr Xian Peoples R China

ISBN: (数字)9781665480536

ISBN: (纸本)9781665480536

With the continuous development of multi-access edge computing (MEC) and artificial intelligence (AI), a new paradigm for 6G, namely endogenous intelligence (EI)-based MEC is created. Hence, a novel EI-based MEC scheme is proposed in this paper. Firstly, we present an EI-based MEC architecture, which deepens EI into MEC architecture. Secondly, referring to service-based architecture (SBA) for 5G core network (5GC), we decouple the edge intelligent services into multiple network functions (NFs) to enhance the flexibility of EI-based MEC architecture. Thirdly, for specific service types, we design MEC templates and instantiation schemes to complete the reconfiguration of EI-based MEC. Finally, we establish a testbed, and experimental results demonstrate that our proposed EI-based MEC can provide users with more reliable and agile edge intelligent services and effectively improve the Quality of Service (QoS).

关键词： Endogenous Intelligence multi-access edge computing Service Based Architecture Template Instance

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Variational Autoencoders for Reliability Optimization in multi-access edge computing Networks

Variational Autoencoders for Reliability Optimization in Mul...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Ahmadi, Arian Semiari, Omid Bennis, Mehdi Debbah, Merouane Univ Colorado Dept Elect & Comp Engn Colorado Springs CO 80918 USA Univ Oulu Dept Commun Engn FI-90014 Oulu Finland Technol Innovat Inst Abu Dhabi U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi 9639 U Arab Emirates

ISBN: (纸本)9781665442664

multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC mandates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users' processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.

关键词： multi-access edge computing Uncertainty Simulation Wireless networks Reliability theory Delays Servers

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Deep Reinforcement Learning-Based Computation Offloading for 5G Vehicle-Aware multi-access edge computing Network

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China Communications 2021年第11期18卷 26-41页

作者： Ziying Wu Danfeng Yan State Key Laboratory of Network and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

multi-access edge computing(MEC)is one of the key technologies of the future 5G *** deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application ***,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to *** with traditional Internet business,these computation tasks have higher processing priority and lower delay *** this paper,we design a 5G-based vehicle-aware multi-access edge computing network(VAMECN)and propose a joint optimization problem of minimizing total system *** view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication ***,the mixed integer nonlinear programming problem is described as a Markov Decision *** show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.

关键词： multi-access edge computing computation offloading 5G vehicle-aware deep reinforcement learning deep q-network

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multi-access edge computing: A Survey

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IEEE access 2020年 8卷 197017-197046页

作者： Filali, Abderrahime Abouaomar, Amine Cherkaoui, Soumaya Kobbane, Abdellatif Guizani, Mohsen Univ Sherbrooke Fac Engn INTERLAB Sherbrooke PQ J1K 2R1 Canada Mohammed V Univ ENSIAS Rabat 10056 Morocco Qatar Univ Comp Sci & Engn Dept Doha Qatar

multi-access edge computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

关键词： 5G mobile communication Network slicing Computer architecture Optimization Quality of service edge computing Bandwidth 5G multi-access edge computing network function virtualization network slicing service function chaining software defined networking

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Computation Offloading Based on Game Theory in multi-access edge computing for 6G Network 14

Computation Offloading Based on Game Theory in Multi-access ...

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14th International Conference on Communication Software and Networks (ICCSN)

作者： Gao, Lixue Chen, Xin Yin, Bo Wu, Bilian Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665453288

multi-access edge computing (MEC) can provide efficient computation offloading service for mobile devices (MDs) in the sixth-generation (6G) network. Owing to ubiquitous connectivity of 6G, dense base stations (BSs) need to be deployed globally. However, it will emerge massive overlapping coverage of BSs, which brings great offloading challenges for MDs in overlapping areas. In this paper, we focus on the computation offloading problem in a 6G-supported MEC system with multiple BSs and multiple MDs, where MDs in the overlapping area of BSs' service scopes. Moreover, a Stackelberg game is formulated to investigate the economic effect and offloading strategic interactions between BSs and MDs. The existence and uniqueness of Stackelberg Equilibrium (SE) are proved by using backward induction approach. In addition, various factors are considered such as the economic incentive of BSs, the social effect of MDs and the delay constraint of tasks. Then, we propose the optimal price and best response algorithm (OPBR) to maximize the utilities of BSs and MDs. Finally, numerical simulation results demonstrate that OPBR algorithm can reach SE and has better performance through comparing with other algorithms.

关键词： 6G multi-access edge computing Stackelberg game Computation offloading

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