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A Potential Game Theoretic Approach to computation offloading Strategy Optimization in End-Edge-Cloud Computing

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第6期33卷 1503-1519页

作者： Ding, Yan Li, Kenli Liu, Chubo Li, Keqin Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China SUNY New York Dept Comp Sci New Paltz NY 12561 USA

Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud computing (EECC) can enhance the utilization of resources and improve quality of experience (QoE). However, the performance of EECC is significantly affected by its architecture. In this article, we classify EECC into two computing architectures types according to the visibility and accessibility of the cloud to UEs, i.e., hierarchical end-edge-cloud computing (Hi-EECC) and horizontal end-edge-cloud computing (Ho-EECC). In Hi-EECC, UEs can offload their tasks only to ESs. When the resources of ESs are exhausted, the ESs request the cloud to provide resources to UEs. In Ho-EECC, UEs can offload their tasks directly to ESs and the cloud. In this article, we construct a potential game for the EECC environment, in which each UE selfishly minimizes its payoff, study the computation offloading strategy optimization problems, and develop two potential game-based algorithms in Hi-EECC and Ho-EECC. Extensive experiments with real-world data are conducted to demonstrate the performance of the proposed algorithms. Moreover, the scalability and applicability of the two computing architectures are comprehensively analyzed. The conclusions of our work can provide useful suggestions for choosing specific computing architectures under different application environments to improve the performance of EECC and QoE.

关键词： Task analysis Computer architecture Optimization Delays Servers Costs Quality of experience computation offloading end-edge-cloud computing (EECC) hierarchical EECC horizontal EECC potential game

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Energy-SLA-aware genetic algorithm for edge-cloud integrated computation offloading in vehicular networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 205-222页

作者： Materwala, Huned Ismail, Leila Shubair, Raed M. Buyya, Rajkumar United Arab Emirates Univ Coll Informat Technol Dept Comp Sci & Software Engn Intelligent Distributed Comp & Syst Induce Res La Abu Dhabi 15551 U Arab Emirates United Arab Emirates Univ Natl Water & Energy Ctr Abu Dhabi 15551 U Arab Emirates New York Univ NYU Dept Elect & Comp Engn Abu Dhabi U Arab Emirates Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst Clouds Lab Melbourne Vic Australia

Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications' performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge-cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications' Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show than the proposed algorithm saves 2.97 times and 1.37 times more energy that the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： computation offloading Edge-cloud computing Energy-efficiency Evolutionary genetic optimizationalgorithm Quality of service (QoS) Vehicular Ad Hoc Networks (VANET)

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computation offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing

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FUTURE INTERNET 2024年第4期16卷 136页

作者： Liu, Yazhi Zhong, Pengfei Yang, Zhigang Li, Wei Li, Siwei North China Univ Sci & Technol Coll Artificial Intelligence Tangshan 063210 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.

关键词： multi-access edge computing computation offloading consistent hashing P2P overlay network multi-dimensional discrete PSO

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Joint Resource and Delay Optimization for computation offloading in Hierarchical MEC Networks Using Stackelberg Game 3rd

Joint Resource and Delay Optimization for Computation Offl...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thanh-Dao Nguyen, Ngoc-Tan Hoang, Trong-Minh Nguyen, Hoai-Son Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Multi-access edge computing (MEC) is a cutting-edge technology integral to 5G and future mobile networks. However, the computational capabilities of edge servers may sometimes be insufficient to meet service delay requirements. As a result, computation tasks need to be offloaded to other MEC servers for processing. In this paper, we introduce a novel offloading solution based on Stackelberg game, i.e., Game-based offloading (GBO), to maximize the processed computation tasks while ensuring the service delay requirements. To tackle the problem, we use the block coordinate descent method to find the optimal solution within given delay constraints. Simulation results demonstrate that under various experimental circumstances, the proposed GBO method significantly outperforms baseline methods, such as the non-offloading and the ODO [9] methods in terms of the probability of successfully processed tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computation offloading

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Energy-Optimized computation offloading with Improved Differential Evolution in UAV-Enabled Edge and Cloud Computing

Energy-Optimized Computation Offloading with Improved Differ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Meijia Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) emerges as a vital paradigm to support the increasing use of mobile users (MUs) with capabilities similar to cloud computing. While most research concentrates on MEC facilitated by terrestrial base stations (BSs), its applicability in scenarios such as disaster rescue and field operations is limited. Efforts have been made to explore MEC assisted by unmanned aerial vehicles (UAVs) with efficient scheduling algorithms. However, relying solely on UAVs for MEC has limitations, particularly for computation-intensive applications. This work proposes a hybrid MEC system lever-aging UAVs and BS. Multiple UAVs and a BS are deployed to provide MEC services directly from UAVs or indirectly from the BS. We formulate an energy-efficient scheduling problem to minimize energy consumption by jointly optimizing UAV trajectories, task associations, and allocation of computing and transmitting resources. To solve it, this work designs a hybrid algorithm named _S_uccess History-based parameter Adaptation for Differential volution with a Niching-based population size reduction strategy and an efficient nsemble sinusoidal scheme (SHADE-NE). Experimental results validate the superiority of SHADE-NE over its benchmark peers, thus proving that SHADE-NE greatly enhances the performance of the system. © 2024 IEEE.

关键词： computation offloading

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Energy Efficient computation offloading for DNN Tasks in Semantic-Assisted Mobile Edge Computing Systems 24

Energy Efficient Computation Offloading for DNN Tasks in Sem...

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Zheng, Guangyuan Wen, Miaowen Tao, Yiwei Li, Qiang Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Jinan University College of Information Science and Technology Guangzhou510632 China

ISBN: (纸本)9798350363760

In this paper, we focus on semantic-aware computation offloading for executing the deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we propose a compress-then-offload mode, in which each user first compresses its raw large-size task data into a small-size semantic feature and then uploads it to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We propose a low-complexity iterative algorithm to solve the original non-convex problem. Simulation results demonstrate that our proposed semantic-aware computation offloading scheme can substantially reduce users' energy consumption compared to the conventional offloading, especially with scarce wireless resources. © 2024 IEEE.

关键词： computation offloading

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Joint computation offloading and Trajectory Planning for Multi-UAV Cooperative Target Search 35

Joint Computation Offloading and Trajectory Planning for Mul...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Liu, Yifei Li, Xiaoshuai Yang, Junan Liu, Hui Hu, Pengjiang Chen, Yuanrui National University of Defense Technology College of Electronic Engineering Hefei China

ISBN: (纸本)9798350362244

Unmanned aerial vehicles (UAVs)-based cooperative target search plays an important role in the scenario of complex and urgent search missions. However, the limited battery life and onboard computational capacity make it challenging for UAVs to accomplish the search mission effectively. This paper investigates the problem of joint computation offloading and trajectory planning for a multi-UAV system integrated with edge computing. Our goal is to minimize the total uncertainty of the target search area while considering the limited energy and search time of UAVs. To adapt to highly dynamic environments, a deep reinforcement learning (DRL) framework is applied in this paper. We propose a novel approach of Action Masks and Branch-network based-Proximal Policy Optimization (AMB-PPO), by which a multi-branch network structure and action masks are introduced into PPO to address the challenges of large joint action spaces and low learning efficiency. The numerical results demonstrate that, when compared to other existing DRL methods, the proposed AMB-PPO algorithm performs better in reducing the total uncertainty of the target search area with higher learning efficiency and faster algorithm convergence. © 2024 IEEE.

关键词： computation offloading

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Secure computation offloading with ETSI MEC+QKD 24

Secure Computation Offloading with ETSI MEC+QKD

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24th International Conference on Transparent Optical Networks, ICTON 2024

作者： Cicconetti, Claudio Conti, Marco Passarella, Andrea Institute of Informatics and Telematics National Research Council Via Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9798350377309

Many modern applications rely on the ability to offload computation-intensive tasks to edge or cloud infrastructures. While this is clearly beneficial from the point of view of the client, because of the decreased load and system requirements, such an approach entails frequent exchanges with the edge/cloud application, which may include sensitive data. The interconnection of the client and remote infrastructures through a Quantum Key Distribution (QKD) network makes it possible to exchange quantum-secure keys between the peer applications. In this paper we study how to achieve this ambitious goal with applications using the Function-as-a-Service (FaaS) programming model in the context of ETSI MEC and QKD standards, as demonstrated in the national project QUANCOM, funded by the Italian Ministry of University and Research. © 2024 IEEE.

关键词： computation offloading

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Joint Social-Aware and Mobility-Aware computation offloading in Heterogeneous Mobile Edge Computing

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IEEE ACCESS 2022年 10卷 28600-28613页

作者： Xu, Chenglin Xu, Cheng Li, Bo Li, Siqi Li, Tao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users' Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users' QoS requirements with the help of deployed MEC facilities is still challenging since mobile users' service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users' willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn-Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users' time and energy consumption in extended real datasets.

关键词： Mobile handsets Task analysis Cloud computing Servers Device-to-device communication Energy consumption Resource management computation offloading social-aware mobile edge computing social networks mobility

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Fairness-oriented computation offloading for cloud-assisted edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 128卷 132-141页

作者： Guo, Kai Zhang, Ruiling Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Luoyang Normal Univ Sch Informat & Technol Luoyang Henan Peoples R China

Mobile edge computing, which enhances the computing power of mobile devices via computation offloading technology, has been proposed as a promising paradigm to alleviate the mismatch between limited computational resources and ever-increasing computational requirements. An appropriate offloading strategy must be identified to optimize the computation offloading performance. Moreover, a mobile user is generally a selfish individual;therefore, we should consider the fairness problem when determining the offloading strategy. In this paper, we focus on computation offloading in a cloud-assisted edge computing system with multiple users, an edge server and a cloud server and aim to optimize the response time for each user. A fairness-oriented approach, including the determination of the application offloading strategy, the data transmission strategy and the cloud-edge cooperation strategy, is proposed. Initially, the computation offloading problem is formulated as a noncooperative game. Then, we find the optimal cloud-edge cooperation strategy and the optimal data transmission strategy based on a theoretical analysis and propose an iterative algorithm to identify the optimal offloading strategy for each user. Finally, we evaluate the proposed approach by means of extensive experiments, and the experimental results show that the proposed approach can significantly reduce the response time of mobile devices. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Cloud-assisted edge computing computation offloading Fairness-oriented approach

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