计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 69-79.doi: 10.11896/jsjkx.220600057

• 边缘智能协同技术及前沿应用 • 上一篇    下一篇

空-天-地一体化移动边缘计算系统的部署优化和计算卸载

郑鸿强, 张建山, 陈星   

  1. 福州大学数学与计算机科学学院 福州 350116
    福建省网络计算与智能信息处理重点实验室 福州 350116
  • 收稿日期:2022-07-30 修回日期:2022-11-08 出版日期:2023-02-15 发布日期:2023-02-22
  • 通讯作者: 张建山(zhangjs0512@163.com)
  • 作者简介:(zhq921579984@163.com)
  • 基金资助:
    国家自然科学基金(62072108);福建省自然科学基金杰青项目(2020J06014);国家重点研发计划(2017YFB1002000)

Deployment Optimization and Computing Offloading of Space-Air-Ground Integrated Mobile Edge Computing System

ZHENG Hongqiang, ZHANG Jianshan, CHEN Xing   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
    Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350116,China
  • Received:2022-07-30 Revised:2022-11-08 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    National Natural Science Foundation of China(62072108),Natural Scienc Foundation of Fujian Province for Distinguished Young Scholars(2020J06014) and National Key R&D Program of China(2017YFB1002000)

摘要: 空-天-地一体化的通信技术作为一种新兴的架构,能够有效提高地面终端的网络服务质量,近年来引起了广泛关注。文中研究了一种空-天-地一体化的移动边缘计算系统,其中多台无人机为地面设备提供低延迟的边缘计算服务,近地轨道卫星为地面设备提供无处不在的云计算服务。由于无人机的部署位置和计算任务的卸载方案是影响系统性能的关键因素,因此需要对无人机的部署位置、地面设备与无人机之间的连接关系以及计算任务的卸载比例进行联合优化,实现系统内系统平均任务响应时延最小化。并且,由于形式化定义的联合优化问题是一个混合非线性规划问题,因此设计了一种双层优化算法,在该算法的上层,提出了一种结合了遗传算法算子的粒子群优化算法来优化无人机的部署位置,并在算法的下层采用贪心算法来实现对计算任务卸载方案的优化。大量的数值仿真实验验证了所提算法的可行性和有效性。结果表明,与其他基准算法相比,所提算法能有效降低系统的任务平均响应时延。

关键词: 空-天-地一体化网络, 移动边缘计算, 无人机部署, 计算卸载

Abstract: As a new architecture,the space-air-ground integrated communication technology can effectively improve the network service quality of ground terminal,and has attracted widespread attention in recent years.This paper studies a space-air-ground integrated mobile edge computing system,in which multiple UAVs provide low-latency edge computing services for ground devices,and low earth orbit satellites provide ubiquitous cloud computing services for ground devices.Since the deployment position of the UAVs and the scheduling scheme of computing tasks are the key factors affecting the performance of the system,the deployment position of the UAVs,the link relationship between the ground terminal and the UAVs,and the offloading ratio of computing tasks need to be jointly optimized to minimize the average task response delay of the system.Since the formally defined joint optimization problem is a mixed nonlinear programming problem,this paper designs a two-layer optimization algorithm.In the upper layer of the algorithm,a particle swarm optimization algorithm that combines genetic algorithm operators is proposed to optimize the deployment position of the UAVs,and the greedy algorithm is used in the lower layer of the algorithm to optimize the computing task offloading scheme.The extensive simulation experiments verify the feasibility and effectiveness of the proposed method.The results show that the proposed method can achieve lower average task response time,compared to other baseline methods.

Key words: Space-Air-Ground integrated network, Mobile edge computing, Unmanned aerial vehicle deployment, Computation offloading

中图分类号: 

  • TP393
[1]XIAO H,HU Z,YANG K,et al.Energy-Aware Joint Routing and Task Allocation Algorithm in MEC Systems Assisted by Multiple UAVs[C]//2020 International Wireless Communications and Mobile Computing (IWCMC).2020:1654-1659.
[2]PORAMBAGE P,OKWUIBE J,LIYANAGE M,et al.Surveyon multi-access edge computing for Internet of things realization[J].IEEE Communications Surveys & Tutorials,2018,20(4):2961-2991.
[3]GUO H Z,LIU J J,ZHANG J.Computation Offloading forMulti-Access Mobile Edge Computing in Ultra-Dense Networks[J].IEEE Communications Magazine,2018,56(8):14-19.
[4]HUANG G,MA Y,LIU X,et al.model-Based Automated Navigation and Composition of Complex Service Mashups[J].IEEE Transactions on Services Computing,2015,8(3):494-506.
[5]ZHANG T K,XU Y,LOO J,et al.Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT[J].IEEE Transactions on Industrial Informatics,2020,16(8):5505-5516.
[6]FENG J,YU F R,PEI Q,et al.Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing:A Deep Reinforcement Learning Approach[J].IEEE Internet of Things Journal,2020,7(7):6214-6228.
[7]LIU L,CHEN C,PEI Q,et al.Vehicular Edge Computing andNetworking:A Survey[J].Mobile Networks and Applications,2021,26:1145-1168.
[8]LI L,WEN X,LU Z,JING W.An Energy Efficient Design of Computation Offloading Enabled by UAV[J].Sensors,2020,20(12):3363.
[9]LI B,FEI Z,ZHANG Y.UAV Communications for 5G and Beyond:Recent Advances and Future Trends[J].IEEE Internet of Things Journal,2019,6(2):2241-2263.
[10]LIU J,SHI Y,FADLULLAH Z M,et al.Space-Air-Ground Integrated Network:A Survey[J].IEEE Communications Surveys &Tutorials,2018,20(4):2714-2741.
[11]CHENG N,LYU F,QUAN W,et al.Space Aerial-AssistedComputing Offloading for IoT Applications:A Learning-Based Approach[J].IEEE Journal on Selected Areas in Communications,2019,37(5):1117-1129.
[12]MEI H,YANG K,LIU Q,et al.Joint Trajectory-Resource Optimization in UAV-Enabled Edge-Cloud System With Virtualized Mobile Clone[J].IEEE Internet of Things Journal,2020,7(7):5906-5921.
[13]MEHRABI M,YOU D,LATZKO V,et al.Device-EnhancedMEC:Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching:A Survey[J].IEEE Access,2019,7(99):166079-166108
[14]PAVEL M,ZDENEK B.Mobile edge computing:A survey onarchitecture and computation offloading[J].IEEE Communications Surveys Tutorials,2017,19(3):1628-1656.
[15]FLORES H,HUI P,TARKOMA S,et al.Mobile code offloa-ding:from concept to practice and beyond[J].IEEE Communications Magazine,2015,53(3):80-88.
[16]PEN Y J,CHEN M,YANG Z H,et al.Energy-Efficient NOMA-Based Mobile Edge Computing Offloading[J].IEEE Communications Letters,2019,23(2):310-313.
[17]YANG G,HOU L,HE X,et al.Offloading Time Optimization via Markov Decision Process in Mobile Edge Computing[J].IEEE Internet of Things Journal,2021,8(4):2483-2493.
[18]ZHANG J,HU X,NING Z,et al.Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks[J].IEEE Internet of Things Journal,2017,5(4):2633-2645.
[19]BI S,ZHANG Y J.Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading[J].IEEE Transactions on Wireless Communications,2018,17(6):4177-4190.
[20]HUANG L,BI S,ZHANG Y.Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks[J].IEEE Transactions on Mobile Computing,2020,19(11):2581-2593.
[21]CHOUHAN S.Energy Optimal Partial Computation Offloading Framework for Mobile Devices in Multi-access Edge Computing[C]//2019 International Conference on Software,Telecommunications and Computer Networks(SoftCOM).2019.
[22]NING Z,DONG P,KONG X,et al.A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things[J].IEEE Internet of Things Journal,2019,6(3):4804-4814.
[23]PANG Y,WANG D,WANG D,et al.A Space-Air-Ground Integrated Network Assisted Maritime Communication Network Based on Mobile Edge Computing[C]// 2020 IEEE World Congress on Services(SERVICES).IEEE,2020:269-274.
[24]NIU Z,SHEN X S,ZHANG Q,et al.Space-air-ground integra-ted vehicular network for connected and automated vehicles:Challenges and solutions[J].Intelligent and Converged Networks,2020,1(2):142-168.
[25]CHEN Q,MENG W,HAN S,et al.Service-Oriented Fair Resource Allocation and Auction for Civil Aircrafts Augmented Space-Air-Ground Integrated Networks[J].IEEE Transactions on Vehicular Technology,2020,69(11):13658-13672.
[26]WANG Z,YU H,ZHU S,et al.Curriculum ReinforcementLearning-Based Computation Offloading Approach in Space-Air-Ground Integrated Network[C]//2021 13th International Conference on Wireless Communications and Signal Processing (WCSP).IEEE,2021:1-6.
[27]ZHOU C,WU W,HE H,et al.Delay-aware IoT task scheduling in space-air-ground integrated network[C]//2019 IEEE Global Communications Conference(GLOBECOM).IEEE,2019:1-6.
[28]ZHOU C,WU W,HE H,et al.Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN[J].IEEE Tran-sactions on Wireless Communications,2020,20(2):911-925.
[29]TANG F,HANS H,NEI K,et al.A Deep Reinforcement Lear-ning-Based Dynamic Traffic Offloading in Space-Air-Ground Integrated Networks (SAGIN)[J].IEEE Journal on Selected Areas in Communications,2022,40(1):276-289.
[30]MAO S,HE S,WU J.Joint UAV Position Optimization and Resource Scheduling in Space-Air-Ground Integrated Networks With Mixed Cloud-Edge Computing[J].IEEE Systems Journal,2021,15(3):3992-4002.
[31]WANG Y,RU Z Y,WANG K,et al.Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing[J].IEEE Transactions on Cybernetics,2020,50(9):3984-3997.
[32]CHEN Z,ZHENG H,ZHANG J,et al.Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems[J].Peer-to-Peer Networking and Applications,2022,15(1):194-205.
[33]CHEN X,ZHANG J,LIN B,et al.Energy-efficient offloading for DNN-based smart IoT systems in cloud-edge environments[J].IEEE Transactions on Parallel and Distributed Systems,2021,33(3):683-697.
[34]SHI Y,EBERHART R.A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings.IEEE,1998:69-73.
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