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Energy-efficient collaborative task offloading in multi-access edge computing based on deep reinforcement learning

AD HOC NETWORKS 2025年 169卷

作者： Wang, Shudong Zhao, Shengzhe Gui, Haiyuan He, Xiao Lu, Zhi Chen, Baoyun Fan, Zixuan Pang, Shanchen China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

In the multi-access edge computing (MEC), task offloading through device-to-device (D2D) communication can improve the performance of edge computing by utilizing the computational resources of nearby mobile devices (MDs). However, adapting to the time-varying wireless environment and efficiently and quickly allocating tasks to MEC and other MDs to minimize the energy consumption of MDs is a challenge. First, we constructed a multi-device collaborative task offloading framework, modeling the collaborative task offloading decision problem as a graph state transition problem and utilizing a graph neural network (GNN) to fully explore the potential relationships between MDs and MEC. Then, we proposed a collaborative task offloading algorithm based on graph reinforcement learning and introduced a penalty mechanism that imposes penalties when the tasks of MDs exceed their deadlines. Simulation results show that, compared with other benchmark algorithms, this algorithm reduces energy consumption by approximately 20%, achieves higher task completion rates, and provides a more balanced load distribution.

关键词： multi-access edge computing Collaborative task offloading Graph neural network Deep reinforcement learning Device-to-Device

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Reinforcement Learning and multi-access edge computing for 6G-Based Underwater Wireless Networks

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IEEE access 2025年 13卷 60627-60642页

作者： Cepeda-Pacheco, Juan Carlos Domingo, Mari Carmen Univ Politecn Catalunya Barcelona Tech UPC Network Engn Dept Castelldefels 08860 Spain Univ Nacl Chimborazo UNACH Coll Engn Riobamba 060150 Ecuador

6G networks are envisioned to dramatically enhance the connectivity landscape by integrating communication across ground, air, and sea environments. In the aquatic domain, the Internet of Underwater Things (IoUT) represents a global network of intelligent underwater devices designed to capture, interpret, and share data. Although Underwater Acoustic Communications (UAC) has become widespread as a solution for transmitting information, data collection from Underwater Sensor Nodes (USNs) to the surface results in extensive delays and higher energy consumption. edge communication emerges as a solution to address these challenges. In this approach, Autonomous Underwater Vehicles (AUVs) bring edge computing as close as possible to the source devices. This paper proposes an innovative AUV-based multi-access edge computing (MEC) system where cluster-heads that collect data from IoUT devices offload their associated computational tasks to local AUVs. These AUVs are strategically positioned to execute tasks either fully locally, partially, or by offloading them entirely to a more resource-equipped AUV (AUV MEC). We achieve this by jointly optimizing the task offloading strategy, resource allocation, and the trajectories of the AUVs. We formulate a non-convex optimization problem to minimize the weighted sum of service delays for all local AUVs and their energy consumption. To address the NP-hard nature of this problem, we employ a deep reinforcement learning algorithm, Deep Deterministic Policy Gradient (DDPG), to solve it. Extensive simulations have been conducted to evaluate the effectiveness of our proposed communication system. The results show that our proposed algorithm outperforms the Total Offloading (Offloading), Local Execution (Locally), and Actor-Critic (AC) algorithms.

关键词： Data collection Delays Energy consumption Resource management Servers multi-access edge computing Cloud computing Data acquisition Autonomous aerial vehicles Energy efficiency Deep deterministic policy gradient (DDPG) multi-access edge computing Internet of Underwater Things (IoUT) autonomous underwater vehicles (AUVs) trajectory optimization

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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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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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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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VR Applications Joint Offloading and Scheduling Optimization in multi-access edge computing*

VR Applications Joint Offloading and Scheduling Optimization...

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IEEE Conference on Wireless Communications and Networking

作者： Rui Zhu Yanzan Sun Shunqing Zhang Xiaojing Chen Guangjin Pan School of Communication and Information Engineering Shanghai University Shanghai P. R. China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

multi-access edge computing (MEC) emerges as an effective computational paradigm. It meets the low latency and low energy consumption requirements of users by enabling user terminals (UE) to offload their computationally intensive applications to nearby access points (AP). However, the integration of Virtual Reality (VR) applications within MEC environments remains underexplored, primarily due to their structural complexity and high computational requirements for offloading and scheduling. To address these challenges, we use Directed Acyclic Graph (DAG) to model VR applications and 6G-oriented Rate-Splitting multiple access (RSMA) to enhance offloading and scheduling processes of VR applications. The objective is to jointly optimize the offloading strategy, transmit power, RSMA decoding order, and UE application scheduling to minimize the total system cost. Recognizing the limitations of traditional learning-based methods, which struggle with convergence in multi-user scenarios, we decompose the optimization problem into two subproblems: application offloading and application scheduling. We then propose the PPOCO algorithm, which integrates reinforcement learning with convex optimization to effectively solve these subproblems independently. Experimental results demonstrate that our proposed method consistently out-performs baseline algorithms in reducing the total system cost across various MEC network configurations.

关键词： Solid modeling Costs multi-access edge computing Processor scheduling Heuristic algorithms Computational modeling Reinforcement learning Convex functions Scheduling Optimization

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An Energy-Efficient Sleeping Strategy for multi-access edge computing

An Energy-Efficient Sleeping Strategy for Multi-Access Edge ...

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IEEE Conference on Wireless Communications and Networking

作者： Shahzeb Javed Pavel Mach Zdenek Becvar Juraj Gazda Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic Department of Computers and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we focus on the scenario with offloading of computationally intensive tasks with delay constraints from users equipment (UEs) to multi-access edge computing (MEC) servers. To avoid user's dissatisfaction with offered quality of service, the computing resources should be able to handle even peak hours. As a result, a dense deployment of MEC servers should be considered in order to bring sufficient computing resources close to the UEs, thus enabling a low delay services. However, at the same time, the dense deployment of powerful MEC servers results, among others, in a high energy consumption. In this paper, we address the high energy consumption problem via a smart sleeping of the MEC servers while preserving quality of service for the UEs. To this end, we determine a set of MEC servers that should stay active and provide computation resources for the offloaded tasks while still meeting UEs requirements on delay. We formulate the problem of selecting the MEC server that can be set into sleep mode to save energy as a minimum set cover problem. Then, we propose a solution to minimize the energy consumption based on branch-and-bound algorithm to activate the MEC servers for computation ensuring the UEs requirement on delay. The effectiveness of the proposed solution is demonstrated through simulations showing that the proposal allows to save up to 34.9% of energy compared to state-of-the-art works while even slightly improving the ratio of offloaded tasks processed within required delay.

关键词： Energy consumption multi-access edge computing Simulation Energy conservation Switches Quality of service Delays Servers Proposals Optimization

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Research and Development of Kubernetes Operator for Lightweight multi-access edge computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Ivan Filianin Nikita Borisov Vladimir Bogatyrev Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight multiaccess edge computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.

关键词： Computer languages multi-access edge computing Codes Computational modeling Machine learning Computer architecture Complex systems Research and development

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multi-access edge computing Handover Strategies, Management, and Challenges: A Review

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IEEE access 2024年 12卷 4660-4673页

作者： Alkaabi, Shaimaa R. Gregory, Mark A. Li, Shuo RMIT Univ Sch Engn Melbourne VIC 3000 Australia

The deployment and operation of multi-access edge computing (MEC) at the network edge provides low latency computing and storage services for end user devices. Support for device mobility is a functional requirement for MEC and a handover strategy that supports the movement of device and application state between MEC nodes is an underlying technology. The handover strategy utilizes an MEC node selection technique and a handover technique to provide a smooth transition between one MEC node to another. Research into MEC handover strategies has focused on algorithms, queuing, and other considerations. The migration of the MEC device and application state is a complex process that requires resource allocation in the destination MEC node that could involve provisioning application instances and, in some scenarios, support for containers or Virtual Machines to be received from the originating MEC node. This paper reviews MEC handover strategies and provides a description of the MEC reference architecture and proposed handover algorithms and techniques found in the literature. MEC handover challenges and gaps in the body of knowledge are discussed to provide guidance for future work.

关键词： Handover multi-access edge computing handover protocol state relocation edge network cloud computing application migration

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multi-access edge computing for Real-Time Applications With Sporadic DAG Tasks - A Graphical Game Approach

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IEEE TRANSACTIONS ON MOBILE computing 2024年第10期23卷 9167-9190页

作者： Asheralieva, Alia Niyato, Dusit Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We consider a multi-operator multi-access edge computing (MEC) network for applications with dependent tasks. Each task includes jobs executed based on logical precedence modelled as a directed acyclic graph, where each vertex is a job, each edge - precedence constraint, such that the job can be started only after its preceding jobs are completed. Tasks are executed by MEC servers with the assistance of workers - nearby edge devices. Each MEC server acts as a master deciding on jobs assigned to its workers. The master's decision problem is complex, as its workers can be associated with other masters in proximity. Thus, the available workers' resources depend on job assignments of all neighboring masters. Yet, as masters select their decisions simultaneously, no master knows concurrent decisions of its neighbors. Besides, some masters can belong to competing operators that have no incentives to exchange information about their decisions. To address these challenges, we formulate a novel framework based on the graphical stochastic Bayesian game, where masters play under uncertainty about their neighbors' decisions. We prove that the game admits a perfect Bayesian equilibrium (PBE), and develop new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each master to reach the PBE independently.

关键词： Task analysis Servers Games Bayes methods Distributed computing Computational modeling Optimization 5G New Radio Bayesian methods distributed computing game theory graph theory incomplete information machine learning multi-access edge computing

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