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Accurate and Reliable Service Recommendation Based on Bilateral Perception in multi-access edge computing

IEEE TRANSACTIONS ON SERVICES computing 2023年第2期16卷 886-899页

作者： Liu, Zhizhong Sheng, Quan Z. Zhang, Zhenxing Xu, Xiaofei Chu, Dianhui Yu, Jian Wang, Shuang Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Macquarie Univ Sch Comp Sydney NSW 2109 Australia Henan Polytech Univ Coll Comp Sci Technol Jiaozuo 454000 Peoples R China Harbin Inst Technol Coll Comp Sci & Technol Weihai 264209 Peoples R China Auckland Univ Technol Dept Comp Sci Auckland 1142 New Zealand Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users' service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.

关键词： multi-access edge computing service recommendation bilateral perception service demand prediction QoS prediction

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6G-Empowered Offloading for Realtime Applications in multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1311-1325页

作者： Huang, Hui Ye, Qiang Zhou, Yitong Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada

multi-access edge computing (MEC) is a promising solution to the resource shortage problem on mobile devices. With MEC, a fraction of the computational tasks on mobile devices could be offloaded to edge servers. Over the past years, a series of machine learning based offloading methods for MEC have been proposed to reduce the completion time of computational tasks. However, most of the existing methods do not work well for realtime applications, which involve tasks with rigorous deadline constraints. In addition, offloading data-intensive tasks via the latest wireless networks, such as LTE and 5G, could lead to unsatisfactory transmission delays. Furthermore, with the state-of-the-art learning-based methods, both the training and inference operation of the learning algorithm are carried out on mobile devices, undesirably leaving less computation resources for computational tasks on mobile devices. In this paper, we propose a 6G-empowered learning-based offloading scheme, MELO, which can be used to make appropriate offloading decisions for realtime tasks. Specifically, the task offloading problem is first formulated as a Markov Decision Process. Thereafter, the problem is solved with a Reinforcement Learning (RL) algorithm, TD3. In addition, 6G is adopted as the communication infrastructure to sufficiently support the data transfer between mobile devices and edge servers. Furthermore, to leave more resources on mobile devices, we devise a novel learning architecture, EALA. With EALA, the training and inference operation of a learning algorithm are decoupled. The training operation is carried out on edge servers while the inference operation is performed on mobile devices. Our experimental results indicate that MELO outperforms the existing offloading methods in terms of task completion time.

关键词： Task analysis Mobile handsets Servers 6G mobile communication Computational modeling Training Delays 6G multi-access edge computing Realtime Applications Offloading edge-assisted Learning

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Secure and Efficient Coded multi-access edge computing With Generalized Graph Neural Networks

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IEEE TRANSACTIONS ON MOBILE computing 2023年第9期22卷 5504-5524页

作者： Asheralieva, Alia Niyato, Dusit Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs' types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) - a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

关键词： Coded distributed computing computational offloading deep learning graph neural networks Internet of Things Lagrange inter-polation machine learning multi-access edge computing resource allocation security

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Task Offloading in multi-access edge computing Enabled UAV-Aided Emergency Response Operations

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IEEE access 2023年 11卷 23167-23188页

作者： Akter, Shathee Kim, Dae-Young Yoon, Seokhoon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea Soonchunhyang Univ Dept Comp Software Engn Asan 31538 South Korea

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAV can perform. The problem is formulated as a non-convex mixed-integer nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush-Kuhn-Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.

关键词： multi-access edge computing task offloading resource allocation messy genetic algorithm

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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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