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A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

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AD HOC NETWORKS 2025年 166卷

作者： Min, Hong Rahmani, Amir Masoud Ghaderkourehpaz, Payam Moghaddasi, Komeil Hosseinzadeh, Mehdi Gachon Univ Sch Comp Seongnam South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan Khajeh Nasir Toosi Univ Technol Dept Informat Technol Tehran Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Duy Tan Univ Sch Comp Sci Da Nang Vietnam Duy Tan Univ DTU AI & Data Sci Hub DAIDASH Da Nang Vietnam

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of multiaccess edge computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi- agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an epsilon-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

关键词： Double Deep Q-Network Vehicular communication multi-access edge computing Resource allocation Task offloading

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Computation Offloading and Band Selection for IoT Devices in multi-access edge computing

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2025年第2期35卷

作者： Ray, Kaustabha Banerjee, Ansuman IBM India Res Lab Bangalore India Indian Stat Inst Kolkata West Bengal India

The advent of multi-access edge computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. Computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two-phase Probabilistic Model Checking-based offloading policy catering to IoT device user preferences. The first stage evaluates the tradeoffs between local vs. server execution while the second stage evaluates the tradeoffs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.

关键词： multi-access edge computing computation offloading probabilistic model checking

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

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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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Availability-Aware Revenue-Effective Application Deployment in multi-access edge computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第7期35卷 1268-1280页

作者： Zhao, Lu Xiao, Fu Li, Bo Zhou, Jian Xu, Xiaolong Yang, Yun Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Victoria Univ Coll Arts Business Law Educ & Informat Technol Melbourne Vic 3000 Australia Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol & Engn Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia

multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novel Availability-aware Revenue-effective Application Deployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is NP-hard. Then, we propose an approximation algorithm named ARAD-A to find the ARAD solution efficiently with a constant approximation ratio of 1/2. We extensively evaluate the performance of ARAD-A against five representative approaches. Experimental results demonstrate that our ARAD-A can achieve the best performance in securing the app vendor's overall revenue.

关键词： Servers Costs Reliability Low latency communication Social networking (online) multi-access edge computing Wireless communication application deployment service availability cost-effective approximation algorithm

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Resource allocation for UAV-enabled multi-access edge computing

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JOURNAL OF SUPERcomputing 2024年第15期80卷 22770-22802页

作者： Falcao, Marcos Souza, Caio Bruno Balieiro, Andson Dias, Kelvin Univ Fed Pernambuco Ctr Informat CIn Ave Jornalista Anibal Fernandes BR-50740560 Recife PE Brazil

In Ultrareliable and Low Latency Communications (URLLC), balancing trade-offs between energy consumption, service availability, and strict reliability and latency requirements is a significant challenge, especially in unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) environments. The constraints imposed by the size, weight and power limitations of UAVs further complicate this task. This study addresses optimizing resource allocation in such environments to meet URLLC demands while minimizing power consumption and maximizing service availability. We explore the virtualization layer of the network function virtualization (NFV)-MEC architecture, incorporating node availability and power consumption alongside conflicting URLLC reliability and latency demands. We introduce an energy-aware model based on continuous-time Markov chain (CTMC) with an embedded virtual resource scaling scheme for Dynamic Resource Allocation (DRA). To solve the optimization problem related to MEC-enabled UAV node dimensioning, we propose a genetic algorithm (GA)-based solution. Our results demonstrate that the proposed GA-based approach achieves a superior balance, with up to a 44% reduction in power consumption compared to the first fit with maximum resources strategy, while also improving service availability and meeting URLLC requirements. This work provides a comprehensive analysis of key virtualization parameters and their impact on critical services within a single NFV-MEC over a UAV node, offering a robust framework for future 6 G network applications.

关键词： multi-access edge computing Ultrareliable and low latency communications Continuous-time Markov chains Network function virtualization Unmanned aerial vehicles

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Proactive Recommendation of Composite Services in multi-access edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2024年第2期17卷 631-644页

作者： Liu, Zhizhong Sheng, Quan Z. Chu, Dianhui Xu, Xiaofei Zheng, Hedan Feng, Kai Yantai Univ Sch Comp & Control Engn Yantai 264005 Shandong Peoples R China Macquarie Univ Sch Comp Sydney NSW 2109 Australia Harbin Inst Technol Coll Comp Sci & Technol Weihai 264209 Shandong Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China

multi-access edge computing (MEC) is an emerging computing paradigm that brings services from centralized cloud to nearby network edge to improve users' Quality of Experience (QoE). With massive services from different domains being emerging in MEC, various powerful composite services can be created with simple services to satisfy users' complex needs. However, existing service composition methods follow a passive service model and cannot proactively recommend optimal composite services to users in MEC, which seriously affect users' service experience. To tackle this issue, we propose an approach for proactive recommendation of composite services based on demand prediction. Our approach consists of three steps. First, we predict a user's service demand based on an attention enhanced deep interaction network (AEDIN) model trained with clustered data. Then, we create the optimal composite service to satisfy the predicted demand with a mobility-aware services composition method, and finally, we proactively recommend the optimal composite service to the user. The extensive experiments have been carried out to verify our proposed approach and prove its performance superiority.

关键词： Predictive models Quality of service Context-aware services Hidden Markov models Clustering algorithms Prediction algorithms Quality of experience multi-access edge computing composite service recommendation service demand prediction deep learning

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Computation Offloading in Resource-Constrained multi-access edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2024年第11期23卷 10665-10677页

作者： Li, Kexin Wang, Xingwei He, Qiang Wang, Jielei Li, Jie Zhan, Siyu Lu, Guoming Dustdar, Schahram Univ Elect Sci & Technol China Lab Intelligent Collaborat Comp Chengdu 611731 Peoples R China Trusted Cloud Comp & Big Data Key Lab Sichuan Prov Chengdu 611731 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110169 Peoples R China Hubei Univ Coll Comp Sci & Informat Engn Wuhan 430062 Peoples R China TU Wien Distributed Syst Grp A-1040 Vienna Austria

Recently, computation offloading methods have greatly improved the Quality of Experience (QoE) in multi-access edge computing (MEC) by offloading tasks to the edge servers. Since well-coordinated actions of Terminal Devices (TDs) are critical to improving the performance of the entire individual system, many practical MEC-based applications, i.e., firefighting robots and unmanned aerial vehicles, require great teamwork among TDs. However, real-world scenarios are usually bound by resource conditions. For instance, network connectivity may weaken or experience interruptions during emergency situations. In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this paper, we propose a computation offloading scheme based on Scheduled multi-agent Deep Reinforcement Learning (SMDRL) to make the most efficient decision in a resource-constrained scenario. First, we design a virtual energy queue based on the MEC system and maximize the QoE (related to service delay and energy consumption) in a real-time manner. Subsequently, we propose a scheduled multi-agent deep reinforcement learning algorithm to support each TD in learning how to encode messages, select actions, and schedule itself based on the received messages. Furthermore, a TopK mechanism is introduced. This mechanism chooses the most crucial TDs to broadcast their messages, and then the computation offloading problem in a communication-constrained MEC environment can be solved in a low-communication manner. Also, we prove that even under limited communication conditions, our proposed methods can still lead to the close-to-optimal performance. The final performance analysis shows that the developed scheme has significant advantages over other representative schemes.

关键词： multi-access edge computing task offloading distributed execution distributed execution resources constrained resources constrained deep reinforcement learning deep reinforcement learning resources constrained deep reinforcement learning

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AoI-Aware Joint Resource Allocation in multi-UAV Aided multi-access edge computing Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 2596-2609页

作者： Shen, Shuai Yang, Halvin Yang, Kun Wang, Kezhi Zhang, Guopeng Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Coll London UCL Dept Elect & Elect Engn London WC1E 6BT England Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

Compared with traditional latency, age of information (AoI) is regarded as a more sufficient metric to measure the freshness of information. In this paper, we investigate the AoI-aware unmanned aerial vehicle (UAV) aided multi-access edge computing (MEC) system, where the UAVs, equipped with MEC servers, provide computing service to the ground IoT devices, which have heterogeneous average peak AoI (APAoI) requirements. According to the Poisson process model, the probabilistic LoS channel model and the M/D/1 queue model, the APAoI of each IoT device is derived, which involves the hovering locations of the UAVs and the communication and computing resources. Then, considering the APAoI requirements of the IoT devices, we formulate the energy consumption minimization problem, in which the offloading strategy and the transmit power of the devices, and the communication and computing resources allocation as well as the hovering locations of the UAVs are jointly optimized. The formulated optimization problem is non-convex. To efficiently solve it, we decompose it into five subproblems and propose an alternative algorithm based on the traditional mathematical method, KKT conditions, and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.

关键词： Internet of Things Autonomous aerial vehicles Optimization Energy consumption Task analysis Resource management Servers Age of information unmanned aerial vehicle multi-access edge computing resource allocation

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A comprehensive review on internet of things task offloading in multi-access edge computing

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HELIYON 2024年第9期10卷 e29916页

作者： Dayong, Wang Abu Bakar, Kamalrulnizam Bin Isyaku, Babangida Eisa, Taiseer Abdalla Elfadil Abdelmaboud, Abdelzahir Univ Teknol Malaysia Fac Comp Dept Comp Sci Johor Baharu 81310 Johor Malaysia Sule Lamido Univ Dept Comp Sci Fac Informat Commun Technol K Hausa Jigawa Nigeria King Khalid Univ Dept Informat Syst Girls Sect Mahayil 62529 Saudi Arabia Sultan Qaboos Univ Humanities Res Ctr Muscat Oman

With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of multi-access edge computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.

关键词： Computation offloading Internet of things Mobile edge computing multi-access edge computing Task offloading

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