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Empirical Evaluation of QUIC-Based Software-Defined Service Migration in multi-access edge computing Over 5G Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第2期33卷 1-25页

作者： Chu, Tran-Tuan Labiod, Mohamed Aymen Augustin, Brice Mathialahan, Kajanan Mellouk, Abdelhamid Univ Paris Est Creteil LISSI TincNET F-94400 Vitry Sur Seine Val De Marne France

Live Service Migration in multi-access edge computing (MEC) environments is essential to ensure seamless service continuity for users in dynamic network scenarios. While advances such as QUIC-based connection migration and CRIU-based container migration have improved reliability, they remain dependent on maintaining active connections with the source server, which is impractical in conditions of high mobility, unstable links, or server outages. Additionally, existing solutions lack a centralized mechanism to optimally manage migration paths. This study introduces the Software-Defined Service Migration (SDSM) paradigm, leveraging a centralized SDN controller, the QUIC protocol, and MEC technologies to enhance service migration. Our approach overcomes the limitations of traditional methods by preemptively coordinating container and connection migrations. Empirical evaluations on a 5G-based testbed and emulated environments demonstrate significant reductions in migration times and improved recovery in mobile scenarios. These results establish SDSM as a robust solution for next-generation network applications, ensuring consistent and adaptive service quality.

关键词： multi-access edge computing Software-defined networking Service migration QUIC protocol OpenAirInterface

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Load-balanced multi-user mobility-aware task offloading in multi-access edge computing

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COMPUTER COMMUNICATIONS 2025年 235卷

作者： Pang, Shanchen Zhou, Meng Gui, Haiyuan He, Xiao Wang, Nuanlai Wang, Luqi China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Shandong Peoples R China State Key Lab Chem Safety Qingdao Peoples R China Shandong Key Lab Intelligent Oil & Gas Ind Softwar Qingdao Shandong Peoples R China

In scenarios with dense user network service requests, multi-access edge computing demonstrates significant advantages in reducing user device load and decreasing service response time. However, the dynamic changes in user trajectories cause edge server load fluctuations, inevitably impacting the overall service processing performance. To tackle this problem, this paper introduces a load-balanced multi-user mobility-aware service request offloading method, achieving efficient service request offloading in mobile user scenarios. Specifically, this paper divides the service request offloading problem into two stages: dynamic edge server allocation and real-time offloading decision generation. In the first stage, users are allocated edge servers based on their location distribution, implementing an adaptive decreasing variance optimization server load balancing algorithm to achieve edge server load balancing. In the second stage, based on the edge server allocation results from the first stage, a latency performance self-optimizing task offloading decision-making algorithm is employed to minimize the processing latency of user requests, utilizing dueling double deep Q-network to generate real-time decisions on whether to offload service requests to the corresponding edge servers. According to experimental results, the proposed algorithm markedly decreases the processing latency of user network service requests in scenarios of different scales, with an average task completion rate of 99.94%. This effectively addresses the problem of inefficient processing requests caused by load fluctuations due to user movement in multi-access edge computing.

关键词： multi-access edge computing Load balance Mobility-aware task offloading Deep reinforcement learning edge server allocation

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Soca: secure offloading considering computational acceleration for multi-access edge computing

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WIRELESS NETWORKS 2025年第2期31卷 1021-1035页

作者： Yi, Meng Yang, Peng Xie, Jinhu Fang, Cheng Li, Bing Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Southeast Univ Coll Software Engn Nanjing 211189 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China

Due to the complexity and security requirements of edge computing environments and the limited resources of terminals, secure offloading in multi-access edge computing (MEC) networks has emerged as a critical and urgent research area. However, many studies on task offloading often ignore the necessary balance between security requirements and efficiency. To address this issue, we propose a Secure Offloading Strategy Considering Computational Acceleration, named SOCA, designed to bolster security while preserving offloading efficiency. Specifically, the secure offloading problem is modeled as a multi-objective optimization problem by achieving a composite function of latency mitigation and security metrics as the optimization objective, which is solved by the ChaCha20-based offloading decision algorithm (ChaCha20-ODA). The algorithm employs the ChaCha20 encryption protocol as its security mechanism. By executing a quarter-round function to generate a keystream, it provides robust protection for data tasks, ensuring that the data remains impervious to malevolent interception by adversaries throughout the transmission process. Furthermore, to improve the computational efficiency of task offloading, the algorithm simultaneously leverages both edge and local computing resources, achieving computational acceleration by optimizing the appropriate offload ratio. The experimental results illustrate that as compared with baselines, our approach achieves remarkable improvement in the balance between latency and safety benchmarks, which demonstrates the superiority of our method.

关键词： multi-access edge computing Security Offloading strategy ChaCha20 Computational acceleration

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Advanced Beamforming and multi-access edge computing: Empowering Ultra-Reliable and Low-Latency Applications in 6G Networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Gunasekaran, K. Dhanasekaran, S. Kumar, R. Vinod Aswath, S. ASAN Mem Coll Engn & Technol Dept Elect & Commun Engn Kancheepuram India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore India Saveetha Engn Coll Dept Elect & Commun Engn Chennai India Veltech Rangarajan Dr Sagunthala R&D Inst Sci & Te Dept Elect & Commun Engn Chennai India

6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. multi-access edge computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource-intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC-enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

关键词： 6th generation base station beamforming multi-access edge computing user equipment

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Cooperative Computation Offloading for multi-access edge computing in 6G Mobile Networks via Soft Actor Critic

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5601-5614页

作者： Sun, Chuan Wu, Xiongwei Li, Xiuhua Fan, Qilin Wen, Junhao Leung, Victor C. M. Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Driven by numerous emerging mobile servicesand applications, multi-access edge computing (MEC) is regarded as a promising technique to alleviate core network congestion and reduce service latency for massive Internet of Things (IoT) over 6G mobile networks. However, the infrastructure of conventional MEC suffers from the lack of cloud server (CS) or cooperation of multiple edge servers (ESs), rendering it less capable of handling large-scale computation tasks in the ultra-dense smart environments. This paper investigates the issues of cooperative computation offloading for MEC in the 6G era. The proposed MEC system enables edge-cloud and edge-edge cooperation to address the limitations of single ES and the nonuniform distribution of computation task arrivals among multiple ESs. To support low-latency services, we model the cooperative computation offloading problem as a Markov decision process, and propose two intelligent computation offloading algorithms based on Soft Actor Critic (SAC), i.e., centralized SAC offloading and decentralized SAC offloading. Evaluation results show that the proposed algorithms outperform the existing computation offloading algorithms in terms of service latency.

关键词： Task analysis 6G mobile communication edge computing Cloud computing Computer architecture Servers Resource management multi-access edge computing 6G cooperative computation offloading service latency Soft Actor Critic.

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A Cooperative Community-Based Framework for Service Caching and Task Offloading in multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3224-3235页

作者： Liao, Zhuofan Yin, Guiying Tang, Xiaoyong Liu, Penglu Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

In multi-access edge computing, services are cached from cloud servers to the edge base station providing low-latency services. Horizontal Collaboration (HC) between Base Stations (BSs) can alleviate the problem of resource limitation in edge base stations. However, the initiative of cooperation between base stations and the interests of base stations themselves are not guaranteed. In this paper, we study the Joint Service Caching and Task Offloading decision Problem (JSCTOP). To solve this problem, we propose a Two-Stage Optimization Framework (TSOF) based on cooperative community idea to maximize the benefit of each base station. In the first stage, a Collaborative Community Mechanism (CCM) is designed based on the Dissimilarity of Service Types (DST) to enhance the collaborative capability of BSs. In the second stage, an algorithm for service caching and task decision to update and replace the Service Types (STs) of BSs. We also design an algorithm for service pricing to incentivize BSs. In this algorithm, BSs are able to profit from providing services to users and also profit from assisting other base stations. Extensive simulation results show that TSOF outperforms its counterparts in average delay, total energy, total benefit, and community.

关键词： multi-access edge computing community partition caching offloading multi-access edge computing community partition caching offloading

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NetDPI: Efficient Deep Packet Inspection via Filtering-Plus-Verification in Programmable 5G Data Plane for multi-access edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2024年第12期23卷 15031-15047页

作者： Zhou, Chengjin Xiang, Qiao Pu, Lingjun Liu, Zheli Zhang, Yuan Yuan, Xinjing Xu, Jingdong Nankai Univ Coll Comp Sci Tianjin 300071 Peoples R China Nankai Univ DISSec Tianjin 300071 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

In this paper, we advocate NetDPI, a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose FIVEFIVE, the first Filtering-plus-Verification algorithm tailored to programmable switches to achieve efficient multiple pattern matching (i.e., the core of DPI). Briefly, the filtering phase introduces a multi-window parallel shift-or algorithm to rapidly screen out all the "suspicious" packet payloads. Meanwhile, the verification phase innovates a level-based state encoding scheme for the Aho-Corasick (AC) algorithm, which substantially increases the number of supported patterns and consequently figures out more "guilty" payloads. We implement the prototype of NetDPI in both software and hardware programmable switches (i.e., BMv2 and Barefoot Tofino2) and make them publicly available. Extensive evaluations indicate that NetDPI provides orders of magnitude improvement in throughput compared to the typical cloud-delivered DPI solutions, and besides FIVE greatly reduces the memory consumption compared to the alternative in-network exact match algorithms under a variety of system settings including different DPI pattern sets and malware-packet percentages.

关键词： Pattern matching 5G mobile communication Random access memory Payloads Cloud computing Hardware Fasteners 5G data plane multi-access edge computing multiple pattern matching programmable switches

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Green Computation Offloading With DRL in multi-access edge computing

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第11期35卷

作者： Yin, Changkui Mao, Yingchi Chen, Meng Rong, Yi Liu, Yinqiu He, Xiaoming Hohai Univ Coll Comp Sci & Software Engn Nanjing Peoples R China SHENZHEN URBAN TRANSPORT PLANNING CTR CO LTD Shenzhen Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing Peoples R China

In multi-access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers;(2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process;(3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS-DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, D4PG$$ {\mathrm{D}}<^>4\mathrm{PG} $$ algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous D$$ D $$ time slots

关键词： computational task offloading deep deterministic policy gradients (DDPG) multi-access edge computing

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Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in multi-access edge computing

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8419页

作者： Na, Minsu Lee, Joohyung Gachon Univ Sch Comp Seongnam 13120 South Korea

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks.

关键词： mobile augmented reality generative AI multi-access edge computing super-resolution

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An optimized heterogeneous multi-access edge computing framework based on transfer learning and artificial internet of things

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 176-190页

作者： Yu, Ping Zhao, Hongwei Geng, Xiaozhong Hu, Ming Yan, Hui Long, Duo Guo, Huangbin Zong, Baoshu Jilin Univ Coll Comp Sci & Technol Changchun 130015 Jilin Peoples R China Changchun Inst Technol Changchun 130012 Jilin Peoples R China Jilin Univ Architecture & Technol Changchun 130114 Jilin Peoples R China Suqian Univ Suqian 223800 Jiangsu Peoples R China

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous multi-access edge computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

关键词： multi-access edge computing Task offloading Transfer learning Artificial intelligence Attention mechanism Deep reinforcement learning Image classification

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