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Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

Digital Communications and Networks 2024年第3期10卷 693-705页

作者： Mian Guo Mithun Mukherjee Jaime Lloret Lei Li Quansheng Guan Fei Ji School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhouChina School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjingChina Universitat Politecnica de Valencia 46022ValenciaSpain South China University of Technology China

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication *** Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value *** MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be *** an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge ***,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the *** addition,different scheduling schemes might provide different computation delays to the offloaded ***,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service *** paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC *** propose a Delay-Greedy computation offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC *** an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio ***,the simulation results show that our proposal can bound the end-to-end delay of various *** under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable *** simulation resul

关键词： Edge computing computation offloading Parallel scheduling Mobile-edge cooperation Delay guarantee

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computation offloading and Pricing Mechanism Based on Stackelberg Game 9

Computation Offloading and Pricing Mechanism Based on Stacke...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Yu, Chuanlong Xie, Gang Liu, Yuanan Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350376548

This article conducts research on satellite pricing mechanism and user computation offloading strategy in low orbit satellite scenarios. The interaction and competition problems between users and LEO satellites are modeled as Stackelberg game and many to one matching game, and the existence of Stackelberg game equilibrium is proved. Finally, a differential evolution pricing mechanism and task computation offloading algorithm based on game theory are proposed, and the feasibility and effectiveness of the proposed algorithm are verified through simulation. © 2024 IEEE.

关键词： computation offloading

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computation offloading and Resource Allocation in MEC-enabled Vehicular Networks: Partial offloading Versus Binary offloading 7

Computation Offloading and Resource Allocation in MEC-enable...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Li, Yangqianhang Li, Li Key Laboratory of Wireless Sensor Network in University of Sichuan Province Sichuan Normal University School of Physics and Electronic Engineering Chengdu China

ISBN: (纸本)9798350372328

By allowing vehicle terminals (VTs) to offload computation tasks to edge servers, mobile edge computing (MEC) effectively alleviates the computational burden on VTs and improves system latency performance in vehicular networks. In this paper, we investigate joint computation offloading and resource allocation problem with different offloading schemes, i.e, partial offloading and binary offloading. We formulate optimization problems to minimize the task completion latency, and obtain the optimal computation offloading and resource allocation solutions. With both offloading schemes, computation offloading and resource allocation algorithms are developed. Numerical results demonstrate that the proposed algorithms exhibit significant latency performance advantages over the benchmark schemes. Additionally, the performances of two offloading schemes are also compared, the results show that partial offloading scheme outperforms binary offloading in latency performances. © 2024 IEEE.

关键词： computation offloading

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Delay-optimal multi-satellite collaborative computation offloading supported by OISL in LEO satellite network

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Journal of Systems Engineering and Electronics 2024年第4期35卷 805-814页

作者： ZHANG Tingting GUO Zijian LI Bin FENG Yuan FU Qi HU Mingyu QU Yunbo School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Yangtze Delta Region Academy Beijing Institute of TechnologyJiaxing 314000China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China China Research and Development Academy of Machinery Equipment Beijing 100089China

By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion *** this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the *** satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle *** results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

关键词： low Earth orbit(LEO)satellite network computation offloading task migration resource allocation

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computation offloading With Instantaneous Load Billing for Mobile Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第3期15卷 1473-1485页

作者： Gao, Mingjin Shen, Rujing Li, Jun Yan, Shihao Li, Yonghui Shi, Jinglin Han, Zhu Zhuo, Li Chinese Acad Sci Inst Comp Technol Beijing 100864 Peoples R China Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Natl Res Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk 634050 Russia Macquarie Univ Sch Engn Sydney NSW 2109 Australia Univ Sydney Camperdown NSW 2006 Australia Pengcheng Lab Shenzhen 518066 Peoples R China Univ Houston Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea Beijing Univ Technol Beijing 100124 Peoples R China

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

关键词： Task analysis Servers Games Delays Edge computing Cloud computing Energy consumption Mobile edge computing computation offloading aggregative game instantaneous load billing

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Multi-agent deep reinforcement learning for computation offloading in cooperative edge network

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2025年第2期63卷 567-591页

作者： Wu, Pengju Guan, Yepeng Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China Shanghai Univ Minist Educ Key Lab Silicate Cultural Rel Conservat Shanghai 200444 Peoples R China

Mobile Edge Computing (MEC) has emerged as an effective paradigm for reducing latency and enhancing computational efficiency. However, the rapid proliferation of edge servers and user devices has significantly increased the complexity of task processing and resource management. Traditional task offloading approaches often rely on centralized decision-making, resulting in high computational complexity and time costs. To address these challenges, this paper introduces a dynamic collaborative framework involving multiple users and edge servers. We formulate the problem of resource allocation and task offloading as a multi-objective Markov Decision Process (MDP) with a mixed action space. To solve this, we propose a novel algorithm called Multi-Agent Mobile Edge Computing (MA-MEC), which leverages multi-agent reinforcement learning. In MA-MEC, each mobile edge server (MES) operates as an independent learning agent. Through centralized training and decentralized execution, these agents collaborate to develop efficient task offloading strategies in complex and dynamic edge environments. Simulation results demonstrate the effectiveness of our approach. MES agents learn to execute tasks more efficiently, increasing the number of processed tasks by 12.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, while task offloading rates rise by 17%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and time costs are reduced by 53%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepa

关键词： Mobile edge computing Multi-agent deep reinforcement learning computation offloading

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MADRL-Based Collaborative computation offloading and Resource Orchestration for Multitask Data Sharing in Smart Agriculture

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3887-3904页

作者： Zhao, Tantan Zhang, Miao He, Lijun Li, Fan Xi An Jiao Tong Univ Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent In Xian 710049 Peoples R China

Multiple different computation tasks may be simultaneously offloaded to mobile edge computing (MEC) servers in smart agriculture scenarios, where the redundant transmission of shared data among different tasks leads to insufficient utilization of system resources (i.e., computing resources, communication resources, and caching resources) and lagging processing efficiency. Existing schemes optimizing multitask computation offloading with shared data almost focus on identical tasks, which are difficult to apply in real-world scenarios with different tasks to meet various service demands of fairness, low latency, and low energy consumption. In this article, we propose a fair, real-time, and green collaborative optimization scheme of computation offloading and resource orchestration for multitask data sharing in smart agriculture based on multiagent deep reinforcement learning (MADRL), aiming to improve offloading efficiency and system resource utilization to meet diverse tasks' service demands. First, a collaborative optimization problem of computation offloading and resource orchestration is formulated to minimize the system latency, energy consumption, and caching space occupancy under constraints of redundant data transmission and limited system resources. It is difficult for traditional optimization methods to solve the formulated optimization problem characterized by dynamics, high-dimensionality, nonlinearity, and mixed-integer. Then, we propose an MADRL algorithm named MATD3-CO-RO-MDS based on a hierarchical reward mechanism to solve it and approximate the optimal offloading and orchestration strategy. Finally, experimental results prove that our proposed algorithm achieves smaller latency, energy consumption, and caching space occupancy compared with existing algorithms. It even has a 76.7% advantage in reducing latency when more tasks participate in offloading.

关键词： Energy consumption Smart agriculture Optimization Multitasking Collaboration Servers Meteorology Approximation algorithms Resource management Temperature distribution computation offloading mobile edge computing (MEC) multiagent deep reinforcement learning (MADRL) multitask data sharing smart agriculture

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A Game-Based computation offloading With Imperfect Information in Multi-Edge Environments

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第1期18卷 1-14页

作者： Lin, Bing Weng, Jie Chen, Xing Ma, Yun Hsu, Ching-Hsien Fujian Normal Univ Coll Phys & Energy Fujian Prov Key Lab Quantum Manipulat & New Energy Fuzhou 350117 Peoples R China Fujian Prov Collaborat Innovat Ctr Adv High Field Fuzhou 350117 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350118 Peoples R China Peking Univ Inst Artificial Intelligence Beijing 100871 Peoples R China Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung 404 Taiwan

Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base stations (BSs), synchronizing the information of all servers takes a long time, which is not applicable to the fluctuant environments. Meanwhile, each server from different BSs is typically selfish and rational, and can only obtain the imperfect information from its adjacent servers, which is a challenge for computation offloading among servers from a global perspective. This article proposes a game-based computation offloading scheme with imperfect information in multi-edge environments. First, a non-cooperative game with imperfect information is designed to analyze the complex interactions during synergistic computation offloading among MEC servers. Second, a Synergistic Balancing offloading Algorithm (SBOA) through distributed decision-making manner to obtain the optimal offloading decision is proposed, which guarantees that the game converges to a Nash Equilibrium (NE) point. Extensive simulation results reveal the fast convergence of SBOA. As the percentage of high-load servers rises and the number of heavy tasks increases, SBOA performs better than other benchmark algorithms in terms of timeliness, effectiveness, and system completion time.

关键词： Servers Internet of Things Games Load management Decision making Performance evaluation Delays Technological innovation Cloud computing Simulation Internet of Things (IoT) mobile edge computing (MEC) computation offloading non-cooperative game imperfect information

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Fairness-Aware computation offloading for Mobile Edge Computing With Energy Harvesting

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IEEE ACCESS 2025年 13卷 55345-55357页

作者： Triyanto, Dedi Mustika, I. Wayan Widyawan, Praphan Pavarangkoon, Praphan Univ Gadjah Mada Dept Elect Engn & Informat Technol Yogyakarta 55281 Indonesia Univ Tanjungpura Dept Comp Engn Pontianak 78124 Indonesia King Mongkuts Inst Technol Ladkrabang Sch Informat Technol Bangkok 10520 Thailand

Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware computation offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.

关键词： Interference Signal to noise ratio Optimization Throughput Energy efficiency Resource management Quality of service Energy harvesting Energy consumption Multi-access edge computing computation offloading fairness index mobile edge computing signal-to-interference-plus-noise ratio throughput

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computation offloading with Bus-Mounted Edge Servers Based on Dung Beetle Optimization in Internet of Vehicles 12

Computation Offloading with Bus-Mounted Edge Servers Based o...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Deng, Xia Bao, Yubin Chen, Shuhong Chang, Le School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China School of Automation Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798331511074

In Internet of Vehicles (IoV), edge computing alleviates congestion in the backbone network and significantly reduces the delay by deploying servers close to users. However, the existing fixed-site and fixed-capacity edge server deployment cannot effectively cope with the spatiotemporal dynamics of IoV end users, i.e., vehicles on road. Therefore, this paper proposes leveraging the inherent mobility of public buses to carry edge servers, and cooperate with fixed-site edge servers to provision elastic joint task offloading service for 1oV end users. We first build a multi-objective optimization model with fixed-site and bus-mounted servers to minimize the user delay, energy consumption, and payment cost. Then, a computation offloading strategy based on the Dung Beetle optimization algorithm is proposed. Taking into account the varying tolerance for delay among different tasks, a preprocessing method is adopted to set the offloading ratio for each task, thereby reducing the search space and accelerating the convergence speed. Furthermore, the maximum acceptable delay constraint is transformed into an unconstrained problem through the penalty function method. Experimental results verify that the proposed computation offloading algorithm outperforms various classical intelligent optimization algorithms in terms of the overall cost. © 2024 IEEE.

关键词： computation offloading

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