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computation offloading and task caching in the cloud-edge collaborative IoVs: A multi-objective evolutionary algorithm

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SIMULATION MODELLING PRACTICE AND THEORY 2025年 141卷

作者： Chai, Zi-xin Chai, Zheng-yi Ren, Junjun Yuan, Dong Northeastern Univ Shenyang 110819 Peoples R China Tiangong Univ Tianjin 300387 Peoples R China Zhengzhou Business Univ Zhengzhou 451200 Peoples R China

With rapid development of Internet of Vehicles (IoVs), various computation-intensive vehicular applications impose great challenges on the limited computing resources of vehicles. To improve the user experience of vehicular applications, the emerging vehicular edge computing (VEC) offloads tasks to roadside edge servers. However, competition over communication and computing resources is inevitable among vehicles. How to make optimal task offloading decisions for vehicles, so as to reduce delay, balance server load and save energy, is worth researching in-depth. In this paper, first, a vehicle-to-vehicle (V2V) communication path acquisition algorithm is designed, and a task caching mechanism introduced which cache some completed applications and related codes on the edge server. Then, a vehicular networking model with joint task caching mechanism for edge-cloud collaboration is proposed. To obtain the near-optimal solutions to this problem, we design a multi-objective evolutionary algorithm based joint task caching and edge-cloud computing decision algorithm (JTCEC-MOEA/D) to maximize the utilities of vehicles. Finally, the proposed algorithm is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The simulation results show that the proposed algorithm can make optimal task offloading-making for vehicles.

关键词： Multi-objective optimization Internet of Vehicles Cloud-edge computing computation offloading Task caching

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computation offloading via Multi-Agent Deep Reinforcement Learning in Aerial Hierarchical Edge Computing Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5253-5266页

作者： Wang, Yuanyuan Zhang, Chi Ge, Taiheng Pan, Miao Univ Sci & Technol China Sch Cyber Sci & Technol Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA

The exponential growth of Internet of Things (IoT) devices and emerging applications have significantly increased the requirements for ubiquitous connectivity and efficient computing paradigms. Traditional terrestrial edge computing architectures cannot provide massive IoT connectivity worldwide. In this article, we propose an aerial hierarchical mobile edge computing system composed of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs). In particular, we consider non-divisible tasks and formulate a task offloading problem to minimize the long-term processing cost of tasks while satisfying the queueing mechanism in the offloading procedure and processing procedure of tasks. We propose a multi-agent deep reinforcement learning (DRL) based computation offloading algorithm in which each device can make its offloading decision according to local observations. Due to the limited computing resources of UAVs, high task loads of UAVs will increase the ratio of abandoning offloaded tasks. To increase the success ratio of completing tasks, the convolutional LSTM (ConvLSTM) network is utilized to estimate the future task loads of UAVs. In addition, a prioritized experience replay (PER) method is proposed to increase the convergence speed and improve the training stability. The experimental results demonstrate that the proposed computation offloading algorithm outperforms other benchmark methods.

关键词： Task analysis Internet of Things Autonomous aerial vehicles Delays Costs Resource management Disasters Aerial computing mobile edge computing deep reinforcement learning computation offloading

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computation offloading in vehicular communications using PPO-based deep reinforcement learning

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-24页

作者： Mustafa, Ehzaz Shuja, Junaid Rehman, Faisal Namoun, Abdallah Bilal, Muhammad Iqbal, Adeel COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Abbottabad 22500 Khyber Pakhtunk Pakistan Southeast Missouri State Univ Dept Comp Sci Cape Girardeau MO USA Islamic Univ Madinah Fac Comp & Informat Syst AI Ctr Madinah 42351 Saudi Arabia Univ Lancaster Sch Comp & Commun Lancaster England Yeungnam Univ Sch Comp Sci & Engn Gyongsan South Korea

Vehicular edge computing enables vehicles to offload compute-intensive tasks to nearby edge servers integrated with roadside units to enhance performance and conserve battery power. In practical environments, vehicles often experience intrinsic task dependencies, stringent deadlines, and speeds and locations within highly dynamic networks, which is still a significant challenge in vehicular communications. Numerical optimization, game theory, and heuristic schemes struggle to meet these diverse and dynamic requirements in optimizing the computation offloading decisions. To this end, we propose a Proximal Policy Optimization (PPO)-based algorithm that optimally manages offloading decisions by employing a policy gradient approach with surrogate clipping to ensure stable and reliable updates. This is crucial in dynamic vehicular networks, where policy updates can significantly impact system performance. Furthermore, we use Generalized Advantage Estimation to further enhance the stability and efficiency by accurately estimating advantages over multiple steps. Our proposed PPO algorithm effectively balances exploration and exploitation and yields optimal offloading decisions while minimizing delays and dropped task ratio. Extensive experiments validate our approach's efficacy, demonstrating substantial improvements in task completion rates and minimizing delays while meeting the requirements of intrinsic task dependencies and stringent deadlines in dynamic vehicular setups. For example, results show that the proposed method surpasses DQN by 13.85%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$13.85\%$$\end{document}, DDQN by 11.24%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepa

关键词： computation offloading Deep reinforcement learning Deep Q networks Proximal policy optimization Vehicular edge computing (VEC) Mobile edge computing (MEC)

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computation offloading for Edge Intelligence in Two-Tier Heterogeneous Networkss

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1872-1884页

作者： Zhao, Junhui Li, Qiuping Ma, Xiaoting Yu, F. Richard Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Peoples R China Coordinat Ctr China CNCERT CC Natl Compter Network Emergency Response Tech Team Beijing 100029 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

Drivenby the increasing need of massive data analysis and the rising concern about data privacy, implementing machine learning (ML) at network edge is drawing increasing attention, where local users are expected to process massive raw data without sharing data to a remote central server. However, due to the limited computing power of user equipments, how to deal with the rich data is a critical problem for each user. Based on computation offloading and edge learning, we propose an edge intelligence (EI) learning framework in two-tier heterogeneous networks to alleviate the computing pressure of users. Focusing on the minimum time delay of model training, we analyze the completion time of local learning in parallel manner and obtain the optimal offloading ratio in the proposed EI framework. Aiming at the strict interference constraint of the macrocell base station (MBS), a priority-based power allocation algorithm is designed. The analysis and simulation results verify the proposed algorithm can improve the data transmission rate and reduce the task completion time while satisfying the interference constraints of the MBS and maximum tolerable delay of learning tasks. In addition, the partial computation offloading can effectively improve the learning accuracy within a given learning time budget.

关键词： computational modeling Task analysis Heterogeneous networks Resource management Servers Data models Delays Edge intelligence computation offloading resource allocation spectrum sharing heterogeneous networks

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computation offloading in blockchain-enabled MCS systems: A scalable deep reinforcement learning approach

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 153卷 301-311页

作者： Chen, Zheyi Zhang, Junjie Huang, Zhiqin Wang, Pengfei Yu, Zhengxin Miao, Wang Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Fuzhou 350002 Peoples R China Fuzhou Univ Fujian Key Lab Network Comp & Intelligent Informat Fuzhou 350116 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England Univ Plymouth Sch Engn Comp & Math Plymouth PL4 8AA England

In Mobile Crowdsensing (MCS) systems, cloud service providers (CSPs) pay for and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of-Service (QoS). Therefore, it is necessary to guarantee security when CSPs and users conduct transactions. Blockchain can secure transactions between two parties by using the Proof-of-Work (PoW) to confirm transactions and add new blocks to the chain. Nevertheless, the complex PoW seriously hinders applying Blockchain into MCS since MDs are equipped with limited resources. To address these challenges, we first design a new consortium blockchain framework for MCS, aiming to assure high reliability in complex environments, where a novel Credit-based Proof-of-Work (C-PoW) algorithm is developed to relieve the complexity of PoW while keeping the reliability of blockchain. Next, we propose a new scalable Deep Reinforcement learning based computation offloading (DRCO) method to handle the computation-intensive tasks of C-PoW. By combining Proximal Policy Optimization (PPO) and Differentiable Neural Computer (DNC), the DRCO can efficiently make the optimal/near-optimal offloading decisions for C-PoW tasks in blockchain-enabled MCS systems. Extensive experiments demonstrate that the DRCO reaches a lower total cost (weighted sum of latency and power consumption) than state-of-the-art methods under various scenarios.

关键词： Mobile crowdsensing Blockchain computation offloading Deep reinforcement learning Model scalability

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computation offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network

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SENSORS 2025年第6期25卷 1722-1722页

作者： Triyanto, Dedi Mustika, I. Wayan Univ Gadjah Mada Dept Elect Engn & Informat Technol Bulaksumur 55281 Yogyakarta Indonesia Univ Tanjungpura Dept Comp Engn Pontianak 78124 Indonesia

Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov-MILP-based short-distance complete local execution algorithm and the full offloading-computation method.

关键词： mobile edge computing computation offloading ultra-dense network resource allocation

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computation offloading and pricing strategy for heterogeneous multicell network with mobile edge computing

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第3期18卷 1-16页

作者： Chen, Minli Zheng, Yifeng Yang, Jingmin Yang, Liwei Zhang, Wenjie Minnan Normal Univ Sch Comp Sci 36 Xianqian St Zhangzhou 363000 Fujian Peoples R China Minnan Normal Univ Key Lab Data Sci & Intelligence Applicat 36 Xianqian St Zhangzhou 363000 Fujian Peoples R China China Agr Univ Coll Informat & Elect Engn 17 Qinghua East Rd Beijing 100091 Peoples R China

The explosive growth of mobile users (MUs) has led to a substantial increase in computing tasks being offloaded to MEC, bringing tremendous pressure to the sustainable development of MEC. To address this problem, the integration of multi-base station (BS) with MEC has attracted widespread attention. In this paper, we propose the joint optimization problem of resource allocation, computation offloading, and resource pricing for multi-BS cooperative MEC in heterogeneous multicell networks. We model the interactions between MEC server, macro base station (MBS), femto base station (FBS), and MUs as a four-stage Stackelberg game, and derive the equilibrium resource pricing and allocation strategies. In the first three stages, we focus on devising optimal resource allocation and pricing strategies to maximize the utility of all stakeholders. In the fourth stage, MUs make an offloading decision to maximize their utility based on the different pricing strategies. The optimization problems in each stage are simplified and proved by derivation using backward induction method. Our approach offers a novel strategy by effectively integrating multiple BSs, enabling a more flexible and efficient resource distribution. The simulation results demonstrate that the proposed multi-BS model can improve the utilities of MBS and MUs by 53% and 50% respectively by introducing FBS.

关键词： Mobile edge computing Heterogeneous cellular networks Stackelberg game computation offloading Resource pricing

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computation offloading for Energy Efficiency Maximization of Sustainable Energy Supply Network in IIoT

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第2期9卷 128-140页

作者： Tong, Zhao Cai, Jinhui Mei, Jing Li, Kenli Li, Keqin Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Hunan Peoples R China Natl Supercomp Ctr Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The efficiency of production and equipment maintenance costs in the Industrial Internet of Things (IIoT) are directly impacted by equipment lifetime, making it an important concern. Mobile edge computing (MEC) can enhance network performance, extend device lifetime, and effectively reduce carbon emissions by integrating energy harvesting (EH) technology. However, when the two are combined, the coupling effect of energy and the system's communication resource management pose a great challenge to the development of computational offloading strategies. This paper investigates the problem of maximizing the energy efficiency of computation offloading in a two-tier MEC network powered by wireless power transfer (WPT). First, the corresponding mathematical models are developed for local computing, edge server processing, communication, and EH. The proposed fractional problem is transformed into a stochastic optimization problem by Dinkelbach method. In addition, virtual power queues are introduced to eliminate energy coupling effects by maintaining the stability of the battery power queues. Next, the problem is then resolved through the utilization of both Lyapunov optimization and convex optimization method. Consequently, a wireless energy transmission-based algorithm for maximizing energy efficiency is proposed. Finally, energy efficiency, an important parameter of network performance, is used as an indicator. The excellent performance of the EEMA-WET algorithm is verified through extensive extension and comparison experiments.

关键词： computation offloading energy efficiency energy harvesting (EH) Lyapunov optimization wireless power transfer (WPT)

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computation offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 6074-6087页

作者： Ernest, Tan Zheng Hui Madhukumar, A. S. Agcy Sci Technol & Res Adv Remfg & Technol Ctr Singapore 138632 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

This paper investigates the energy efficiency of computation offloading strategies in multi-access edge computing-enabled (MEC-enabled) Internet-of-Vehicles (IoV) networks. First, the energy efficiency of computation offloading strategies in the MEC-enabled IoV network are derived in closed-form. Thereafter, a multi-agent deep reinforcement learning based (MADRL-based) energy efficiency maximization algorithm is proposed to enable computation offloading strategies to attain maximum energy efficiency in the MEC-enabled IoV network. It is shown through extensive analysis that the maximum attained energy efficiency hinges on the choice of task size and transmission timeout threshold, with a computation offloading strategy that jointly considers transmission and computation latencies outperforming existing strategies. It is also shown that the proposed MADRL-based energy efficiency maximization algorithm achieves near-optimal energy efficiency in the MEC-enabled IoV network, making it a promising solution towards achieving energy efficient MEC-enabled IoV networks.

关键词： Task analysis Servers Resource management Uplink Cellular networks Signal to noise ratio Interference Multi-access edge computing computation offloading Internet-of-Vehicles vehicular networks multi-agent deep reinforcement learning energy efficiency

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computation offloading and Resource Allocation in Failure-Aware Vehicular Edge Computing

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1877-1888页

作者： Tang, Chaogang Yan, Ge Wu, Huaming Zhu, Chunsheng China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China

The advent of Intelligent Cyber-Physical Transportation Systems (ICTS) has not only accelerated the reformation and evolvement of smart transportation, but also ushered in a new era of vehicular applications. These applications typically impose stringent latency requirements and demand substantial computing resources. Vehicular edge computing (VEC) has emerged as an efficient solution to address these challenges, leveraging its inherent ability to provide ultra-low latency services. Existing studies primarily concentrate on either optimizing resource allocation or minimizing response latency, while ignoring the fact that the task execution in VEC is more susceptible to failures compared to cloud computing environments. Accordingly, we design a cost-efficient and failure-resistant task offloading strategy for VEC systems with the goal of minimizing the average response latency for all tasks. Specifically, our problem is modeled as a nonlinear multi-constraint continuous optimization problem, with tightly coupled optimization variables in the objective function and constraints. To tackle this issue, we initially decompose the optimization problem into per-slot optimization subproblems. Subsequently, we employ an effective algorithm with low time complexity to solve these subproblems in a slot-by-slot manner. We comprehensively evaluate the performance of our approach through extensive simulations, demonstrating that our method outperforms the baseline approaches in various aspects.

关键词： Task analysis Optimization Resource management Servers Quality of experience Edge computing computational modeling ICTS computation offloading resource allocation failure-aware vehicular edge computing

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