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multi-access edge computing Resource Slice Allocation: A Review

IEEE access 2024年 12卷 188572-188589页

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

The global deployment of fifth generation mobile cellular (5G) and beyond (5GB) communication systems has accelerated the growth of the Internet of Things (IoT) and the Internet of Everything (IoE). New applications and services that add complexity to the network, and require low latency, have necessitated new architectures that facilitate dynamic resource allocation. The network edge is changing, as multi-access edge computing (MEC) is deployed with new responsive designs that include dynamic resource slice allocation that supports the varying needs of new and innovative applications and services. Augmented reality, extended reality and virtual reality applications are key drivers for the changing network architecture. IoT and IoE data aggregation and analysis at the network edge has required new approaches to designing edge network architectures that achieve seamless access anywhere and anytime, ultra-low latency, edge computing and storage, and ultra-high bandwidth. MEC and Network Slicing (NS) are evolving to provide a key part of the solution to the new challenges. MEC allows operators to connect user devices to edge cloud computing and storage systems with low latency and high bandwidth. However, a practical configuration is required to facilitate cloud-based applications and services at the network edge and to address end-user demands. The MEC resources may be flexibly and dynamincally partitioned into separate slices. This paper will review how the integration of MEC and NS can be utilised for efficient resource allocation in edge networks. This article comprehensively reviewed the architecture of network slicing and edge computing. A review of MEC and NS in support of 5G and 5GB has been included and current challenges identified. This article aims to serve as an insightful guide to inspire further research into 5GB communication systems and edge computing.

关键词： Resource management 5G mobile communication Network slicing Ultra reliable low latency communication Radio access networks Network function virtualization Internet of Things Cloud computing Surveys Reviews multi-access edge computing 5G 6G resource allocation radio access network

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Ruin Theory for User Association and Energy Optimization in multi-access edge computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 12436-12440页

作者： Kim, Do Hyeon Manzoor, Aunas Alsenwi, Madyan Tun, Yan Kyaw Saad, Walid Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 Gyeonggi Do South Korea Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA

In this correspondence, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: 1) user association phase and 2) task offloading phase. In the first phase, a ruin theory-based approach is developed to obtain the users association considering the users' transmission reliability and resource utilization efficiency. Meanwhile, in the second phase, an optimization-based algorithm is used to optimize the data offloading process. In particular, ruin theory is used to manage the user association phase, and a ruin probability-based preference profile is considered to control the priority of proposing users. Here, ruin probability is derived by the surplus buffer space of each edge node at each time slot. Giving the association results, an optimization problem is formulated to optimize the amount of offloaded data aiming at minimizing the energy consumption of users. Simulation results show that the developed solutions guarantee system reliability, association efficiency under a tolerable value of surplus buffer size, andminimize the total energy consumption of all users.

关键词： Ruin theory convex optimization user association energy consumption multi-access edge computing

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edge-Cloud Offloading: Knapsack Potential Game in 5G multi-access edge computing

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第11期22卷 7158-7171页

作者： Hsieh, Cheng-Ying Ren, Yi Chen, Jyh-Cheng Natl Yang Ming Chiao Tung Univ NYCU Dept Comp Sci Hsinchu 30010 Taiwan Univ East Anglia UEA Sch Comp Sci Norwich NR4 7TJ England

In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high- and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edge-cloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG's performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.

关键词： multi-access edge computing QoS 5G performance analysis 3GPP standards

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Toward Deploying Parallelized Service Function Chains Under Dynamic Resource Request in multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1899-1910页

作者： Zhang, Dongliang Wang, Lei Rezaeipanah, Amin Henan Open Univ Sch Informat Engn & Artificial Intelligence Zhengzhou 450046 Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454003 Peoples R China Persian Gulf Univ Dept Comp Engn Bushehr Iran

Resource distribution policy and how to assemble the Service Function Chain (SFC) in multi-access edge computing (MEC) networks to meet service quality standards poses an important challenge for Network Function Virtualization (NFV) technology. Increasing the number of Virtual Network Functions (VNFs) leads to high-latency SFC assembly, which can be countered by network function parallelization. However, existing studies parallelize VNF for resource allocation in MEC by assuming that the demanded resources do not change during SFC assembly. To address these issues, this paper develops a Latency-aware VNF Parallelization strategy under Resource demand Uncertainty (LVPRU) in MEC. We formulate LVPRU under the assumption of resource uncertainty in MEC via Quadratic Integer Programming (QIP) and show that the problem is NP-hard. LVPRU parallelizes VNFs by discovering dependencies between them and assembles multiple sub-SFCs instead of the original SFC. We apply Asynchronous Advantage Actor-Critic (A3C) as a deep reinforcement learning algorithm to assemble sub-SFCs. We finally evaluate the performance of LVPRU through trace-driven simulations. The evaluation results of proposed strategy are promising in different scenarios compared to benchmark algorithms.

关键词： Uncertainty Costs Heuristic algorithms Throughput Resource management Assembly Service function chaining Quality of service multi-access edge computing Middleboxes MCE NFV SFC DRL network function parallelization resource demand uncertainty

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Safety-Critical Offloading with Constrained Reinforcement Learning for multi-access edge computing

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ACM TRANSACTIONS ON SENSOR NETWORKS 2025年第2期21卷 1-37页

作者： Huang, Hui Ye, Qiang Zhou, Yitong Dalhousie Univ Fac Comp Sci Halifax NS Canada

The proliferation of computation-intensive applications, such as autonomous driving, has urged mobile devices to alleviate their local computation pressure using external computing resources. As a promising solution, multi-access edge computing tackles this problem by offloading computational tasks from mobile devices to edge servers. However, existing offloading schemes suffer from two fundamental limitations. First, they lack built-in measures to prevent deadline misses. For safety-critical applications, including autonomous driving, a deadline miss could result in catastrophic consequences. Second, existing schemes typically update offloading policies periodically. Namely, a policy based on the current system state is generated for a time window consisting of multiple time slots. Since system states could change from one time slot to the next one, the generated policy might not work well during the entire window. In this article, we propose a novel offloading scheme for safety-critical applications, Constrained Reinforcement Learning-based Offloading (CRLO). With CRLO, a safety layer is added to the learning-based policy generator, which effectively eliminates deadline misses. Furthermore, a long-sequence forecasting model, Informer, is utilized to predict temporally dependent system states, which helps to generate appropriate offloading policies. Our experimental results indicate that CRLO outperforms existing schemes in terms of deadline satisfaction and task completion time.

关键词： Constrained reinforcement learning multi-access edge computing real-time applications task offloading

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Data Offloading in UAV-Assisted multi-access edge computing Systems Under Resource Uncertainty

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IEEE TRANSACTIONS ON MOBILE computing 2023年第1期22卷 175-190页

作者： Apostolopoulos, Pavlos Athanasios Fragkos, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex multi-access edge computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user's satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework's consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users' experienced performance compared to alternative approaches.

关键词： Data offloading multi-access edge computing unmanned aerial vehicles risk-aware behavior computing uncertainty prospect theory convex optimization

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Graph Tasks Offloading and Resource Allocation in multi-access edge computing: A DRL-and-Optimization-Aided Approach

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3707-3718页

作者： Li, Jinming Gu, Bo Qin, Zhen Han, Yu Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 51800 Peoples R China Guangdong Prov Key Lab Fire Sci & Intelligent Emer Guangzhou 510006 Peoples R China

Timely and appropriate task offloading is difficult to achieve due to strong coupling between tasks, uncertainty of computing resources and the dynamics of environment. In this article, a multi-access edge computing (MEC) system based on vehicle-to-infrastructure (V2I) communication is considered, where a group of correlated tasks are generated by task initiators (TIs) and then offloaded to different task executors (TEs). This study aims to jointly optimize the graph task assignment and resource allocation in MEC systems to minimize the weighted sum of delay and energy consumption (WDEC). The problem is formulated as a mixed-integer nonlinear programming problem (MINLP). To derive a feasible solution, the optimization process is decomposed into two stages. First, an off-policy algorithm based on deep reinforcement learning (DRL) framework is proposed to solve task offloading problem. Notably, the proposed algorithm relies on neither complete communication status nor prior knowledge of computing resources;Second, given the offloading decisions, the optimal transmission power is obtained by relaxing the convex optimization problem. Extensive experiments verify the superiority of the proposed method, compared with existing algorithms.

关键词： multi-access edge computing Reinforcement learning Deep learning Internet of Vehicles deep reinforcement learning directed acyclic graph internet-of-vehicles resource allocation

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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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Service-Aware Cooperative Task Offloading and Scheduling in multi-access edge computing Empowered IoT 1

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23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Chen, Zhiyan Tao, Ming Li, Xueqiang He, Ligang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

multi-access edge computing(MEC) enables computation task offloading and data processing at close proximity to provide rich endusers services with ultra-low latency in Internet of things(IoT). However, the high heterogeneity of the edge node configuration and the diversity of services pose challenges in fully utilizing the computing capacity in MEC. In this paper, we consider the problem of service-aware cooperative task offloading and scheduling in a three-tier MEC empowered IoT where the service requests from IoT devices can be distributed among edge nodes or further offloaded to remote cloud. As this problem is proven to be NP-hard, we proposed a two-layer Cooperative workload Initialization and Distribution Algorithm (CIDA) to solve the problem with low time complexity by decomposing it into two subproblems: 1) the optimization problem of offloading profile under dynamic resource allocation determined by the workload type, and 2) optimization problem of computation resources allocation under given offloading profile. Extensive experiments demonstrate that CIDA achieves superior performance compared to other approaches and scales well as the system size increases.

关键词： multi-access edge computing Services-aware offloading Task scheduling Resource allocation

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A multi-access edge computing Approach to Intelligent Tutoring Systems for Real-Time Adaptive Learning

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International Journal of Information Technology (Singapore) 2025年第4期17.0卷 2117-2128页

作者： Singh, Ramesh Konyak, Chenlep Yakha Longkumer, Akangjungshi Computer Science and Engineering School of Engineering and Technology Nagaland University Meriema Nagaland Kohima 797004 India

Intelligent tutorial systems (ITS) lack real-time adaptive learning features to provide better learning experiences to the learners. To address this problem, a novel flexible learning system (FLS) is proposed which supports real-time adaptive learning in ITS using a multi-access edge computing (MEC) approach. The adaptive features create personalized learning styles and content tailored to each student’s context, facilitating one-to-one learning. The MEC environment allows the FLS to process tasks for content generation with real-time responses while optimizing network bandwidth usage. The proposed FLS reduced task processing time by 94%, enabling real-time responses that facilitate flexible learning experiences. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： E-learning Intelligent tutor system Learning style multi-access edge computing

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