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multi-access edge computing: A Survey

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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multi-access edge computing Architecture, Data Security and Privacy: A Review

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IEEE access 2021年 9卷 18706-18721页

作者： Ali, Belal Gregory, Mark A. Li, Shuo RMIT Univ Sch Engn Melbourne Vic 3000 Australia

multi-access edge computing (MEC) is an extension of cloud computing that aims to provide computation, storage, and networking capabilities at the edge of the network in close proximity to end-users. The MEC architecture supports applications and services that bridge between cloud computing and end-users. The architecture includes devices and systems that are interconnected, layered, and flexibly deployed. As a result of the technological advancements, MEC is facing a myriad of highly sophisticated threats. This paper provides a review of MEC Architecture, use cases, conceptual guidelines for MEC security architecture, security and privacy techniques, and identifies current and future challenges, their implications, and approaches to overcome the challenges. This research examined significant threats, described the MEC architecture, identified the susceptible functional layers, the different categories of threats, and the potential security safeguards. The research recommends that MEC providers should implement multiple layers of security controls to mitigate targeted attacks.

关键词： Security Computer architecture Data security Cloud computing Servers Data privacy edge computing multi-access edge computing MEC security privacy SDN 5G

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multi-access edge computing enabled internet of things: advances and novel applications

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NEURAL computing & APPLICATIONS 2020年第19期32卷 15313-15316页

作者： Zhu, Rongbo Liu, Lu Song, Houbing Ma, Maode South Cent Univ Nationalities Wuhan Peoples R China Univ Leicester Leicester Leics England Embry Riddle Aeronaut Univ Daytona Beach FL USA Nanyang Technol Univ Singapore Singapore

multi-access edge computing (MEC) is emerging as a new and promising methodology with the development of Internet of Things (IoT) and smart devices, which enables a large class of applications and has emerged with a significance to change our life, and also is raising the challenging research issues due to the requirements of low latency, deployment independence, location awareness and mobility support. However, recent advances on distributed artificial intelligence (AI), big data, and edge/terminal cooperation schemes show that IoT still struggles with fundamental, long-standing problems, such as computational models, resource allocation and scheduling, power efficient data processing and cooperative decision-making mechanisms in edge. How to enhance IoT with MEC, distributed AI, cooperation and novel applications is worth exploring. The objective of this special issue is to report high-quality state-of-the-art research contributions that address these key aspects of MEC-enabled IoT and novel applications. By presenting a selection of papers on various topics related to MEC-enabled IoT, we hope to shed light on the multiple aspects of this emerging paradigm. The papers included in this issue propose solutions for resource management and allocation, multimedia processing, as well as novel applications.

关键词： Internet of things multi-access edge computing Novel applications

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multi-access edge computing for UAVs Cooperation in Power System 6

Multi-Access Edge Computing for UAVs Cooperation in Power Sy...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Wang, Wendi Zhu, Hong Zhou, Dongxu Wei, Lei Xu, Yang Hui, Yang Wang, Haizhou State Grid Jiangsu Elect Power CO Ltd Nanjing Power Supply Co Nanjing Peoples R China State Grid Jiangsu Elect Power CO Ltd Nanjing Peoples R China NARI Informat & Commun Technol Co Ltd Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

multi-access edge computing (MEC) uses unmanned aerial vehicles(UAVs) as edge nodes for dynamic deployment, which can cover complex environments and greatly enhance the application scope. Rotary-wing UVAs are now commonly used, but they cannot load heavy MEC servers. Therefore, the paper proposes the research of MEC of UAVs cooperation applied in power system. By taking advantage of the flexibility and high load of the fixed-wing UAV, it can be deployed in the environment with wide range, sparse distribution of power equipment(PE) and difficult communication, so as to support the use of PE. This is because the MEC servers loaded by UVAs have a low data processing capability. Therefore, tasks uploaded by PE often need to wait in a queue for a long time. However, the priorities of power services vary and cannot be processed equally. So combining real-time scheduling algorithm, simulated annealing(SA) algorithm and differential evolution(DE) algorithm, the paper proposes an MEC offloading algorithm assisted by UVAs for PE which have different priorities. By flexibly adjusting the order of power tasks in the waiting queue, the best result can be obtained under the conditions of meeting the delay requirements of all PE and making the high priority tasks process faster.

关键词： multi-access edge computing fixed-wing UAV queue scheduling differential evolution

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multi-access edge computing Empowered Integrated Hybrid Sensing and Communication: A Computation-Efficient Design

Multi-Access Edge Computing Empowered Integrated Hybrid Sens...

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International Conference on Ubiquitous Communication (Ucom)

作者： Dou, Chenglong Huang, Xumin Wu, Yuan Qian, Liping Quek, Tony Q. S. Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Zhejiang Univ Technol Informat Engn Hangzhou Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9798350374223;9798350374216

Integrated sensing and communication (ISAC) system has been emerged as a crucial paradigm for addressing the growing demand of emerging wireless applications that require both ultra-reliable data transmission and high-precision sensing. However, due to the limited computation capability of ISAC devices, the large amount of collected sensing data is difficult to be timely processed. In this paper, we consider that the ISAC device can offload the sensing data to a group edge helper nodes via multi-access edge computing to improve the efficiency of sensing data processing. We propose a multi-access edge computing empowered ISAC with hybrid active and passive sensing, in which the ISAC device can perform passive sensing through the sensing reflected signal from the edge helper nodes while performing active sensing. To investigate this problem, we formulate a joint optimization of the transmit beamforming for active sensing, passive sensing and offloading, as well as the computation rates of both the ISAC device and the edge helper nodes, with the objective of maximizing the total computation rates for the sensing data. Despite the non-convexity of the formulated problem, we propose an efficient algorithm to obtain its solutions. Simulation results validate the performance advantages of our multi-access edge computing empowered integrated hybrid sensing and communication.

关键词： Integrated sensing and communication (ISAC) multi-access edge computing hybrid active and passive sensing

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Mobility-Aware and Code-Oriented Partitioning Computation Offloading in multi-access edge computing

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JOURNAL OF GRID computing 2022年第2期20卷 1-15页

作者： Liu, Yaqin Liu, Chubo Liu, Jing Hu, Yikun Li, Kenli Li, Keqin Hunan Univ Dept Informat Sci & Engn Changsha Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA Wuhan Univ Sci & Technol Dept Comp Sci & Technol Wuhan 430065 Peoples R China

multi-access edge computing (MEC) enables less resourceful smart mobile devices (SMDs) to use computation and memory intensive applications by offloading them to edge servers with tolerant latency, significantly improving the computing paradigm of SMDs. But, when involving mobility in MEC, offloading strategy and overhead can be significantly influenced by the movements of SMDs. What(')s more, the movements of SMDs make it harder to deal with precedence among subtasks. However, to the best of our knowledge, few articles have studied mobility management in code-oriented partitioning offloading. To give an efficient solution, we propose the cost-saving offloading policy with mobility prediction using convex optimization and Lagrangian approach. Our scheme can help moving SMDs in MEC like driverless vehicles efficiently complete their tasks and reveal the impact of task dependency on completion time. The experimental results show that our algorithm can achieve at least 12% performance improvement on average than other three common methods.

关键词： Computation offloading multi-access edge computing Mobility management Resource allocation Task dependency

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On multi-access edge computing: A Survey of the Emerging 5G Network edge Cloud Architecture and Orchestration

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2017年第3期19卷 1657-1681页

作者： Taleb, Tarik Samdanis, Konstantinos Mada, Badr Flinck, Hannu Dutta, Sunny Sabella, Dario Aalto Univ Espoo 06220 Finland Sejong Univ Seoul 143747 South Korea Huawei European Res Ctr D-80992 Munich Germany Aalto Univ Espoo 06220 Finland Nokia Bell Labs FI-02610 Espoo Finland Intel D-85579 Munich Germany

multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

关键词： MEC multi-access edge computing Mobile edge computing 5G Mobile Communications edge computing Application Virtualization Network Function Virtualization Software Defined Networking Network Softwarization

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Context-Aware Fault Classification for multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第6期21卷 6290-6300页

作者： Ray, Kaustabha IBM Res India Bengaluru 560045 India

multi-access edge computing (MEC) is increasingly being adopted as the de facto enabler for ultra-low latency access to application services. By placing application services on MEC servers situated in proximity to end users, MEC avoids the large network latencies frequently experienced while accessing cloud services. MEC is envisioned as the fundamental enabler for a number of ultra-low latency safety-critical systems, including data inferencing for autonomous vehicles amongst others. The MEC paradigm is, however, highly susceptible to various types of faults such as MEC server downtime, communication link faults, network hardware faults and so on owing to the heterogeneity of hardware configurations and diverse geographies of operations. For real-time and safety-critical workloads, averting the impact of faults is a key facet. To address this challenge, we synthesize a fault classification policy for MEC that categorizes a fault as critical requiring immediate rectification or non-critical by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees with respect to a specified failure context. We present experimental results on a real-world datasets to show the effectiveness of our approach.

关键词： Servers Object recognition Probabilistic logic Model checking Containers Social networking (online) Quality of experience multi-access edge computing fault classification probabilistic model checking

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Automated Application Deployment on multi-access edge computing: A Survey

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IEEE access 2023年 11卷 89393-89408页

作者： Santos, Alvaro Bernardino, Jorge Correia, Noelia Coimbra Inst Engn Polytech Inst Coimbra P-3030199 Coimbra Portugal Univ Algarve Ctr Elect Optoelect & Telecommun CEOT P-8005139 Faro Portugal

The advent of technologies such as 5G makes it possible to improve the availability and quality of existing services and introduce new ones, such as enhanced mobile broadband, IoT applications, augmented reality, mission-critical services, cloud gaming, or smart infrastructure. It is now possible to get faster responses to a wide range of requests, but the addition of more users and services can still make normal operation difficult due to network congestion, bandwidth limitations, scalability issues, service differentiation, or security concerns. To solve this problem, and to help meet Service Level Agreements (SLAs), some services must be brought closer to the user. The multi-access edge computing (MEC) initiative is a step in this direction, enabling cloud-like services to be moved closer to the end user, providing lower access latencies. However, the dynamic nature of edge computing environments, and the mobility of users, require the implementation of automated service delivery processes that can adapt to environmental conditions, such as the use of optimal policies tailored to the scenario. This article surveys the research focused on MECs, particularly those that use automated deployment mechanisms, namely Infrastructure as Code (IaC) tools. Today, these tools play a key role in automated deployment mechanisms, especially for maintaining optimal policies, which is still under investigation. The result of this assessment has been the identification of the relevance of IaC to these processes and the identification of future research directions.

关键词： Cloud computing Bandwidth Security multi-access edge computing Standards Quality of service Surveys infrastructure as code automated deployments

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FedICT: Federated multi-Task Distillation for multi-access edge computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第6期35卷 952-966页

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Pan, Quyang Jiang, Xuefeng Gao, Bo Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China Zhongguancun Lab Beijing 100190 Peoples R China Beijing Jiaotong Univ Engn Res Ctr Network Management Technol High Speed Sch Comp & Informat Technol Minist Educ Beijing 100044 Peoples R China

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in multi-access edge computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated multi-task Distillation for multi-access edge computing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Extensive experiments on three datasets demonstrate that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT in all considered scenarios.

关键词： Computational modeling Data models Training Servers multitasking Adaptation models Optimization Distributed optimization federated learning knowledge distillation multi-access edge computing multi-task learning

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