基于最优运输理论的物联网边缘计算资源优化机制

张琪; 蒋宇娜; 葛晓虎; 李永会

doi:10.11959/j.issn.2096-3750.2021.00225

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基于最优运输理论的物联网边缘计算资源优化机制

专题：物联网边缘智能与雾计算技术 | 更新时间：2024-06-05

- 基于最优运输理论的物联网边缘计算资源优化机制
- Resource allocation based on optimal transport theory in IoT edge computing
- 物联网学报 2021年5卷第2期页码：60-70
- 作者机构：
  
  1. 华中科技大学，湖北武汉 430074
  2. 澳大利亚悉尼大学，澳大利亚悉尼 NSW
- 作者简介：
  
  [ "张琪（1997- ），女，华中科技大学硕士生，主要研究方向为无线通信、环境智适应网络柔性传输理论和边缘计算" ]
  [ "蒋宇娜（1994- ），女，华中科技大学博士生，主要研究方向为无线通信、区块链和物联网" ]
  [ "葛晓虎（1972- ），男，博士，华中科技大学教授，主要研究方向为移动通信、无线网络中的流量建模、绿色通信等" ]
  [ "李永会（1975- ），男，博士，澳大利亚悉尼大学教授，主要研究方向为无线通信、物联网、无线AI等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U2001210)
- DOI：10.11959/j.issn.2096-3750.2021.00225
  中图分类号： TN92
- 纸质出版日期：2021-06-30，
  
  网络出版日期：2021-06，
- 稿件说明：
移动端阅览
张琪, 蒋宇娜, 葛晓虎, 等. 基于最优运输理论的物联网边缘计算资源优化机制[J]. 物联网学报, 2021,5(2):60-70.

QI ZHANG, YUNA JIANG, XIAOHU GE, et al. Resource allocation based on optimal transport theory in IoT edge computing. [J]. Chinese journal on internet of things, 2021, 5(2): 60-70.
张琪, 蒋宇娜, 葛晓虎, 等. 基于最优运输理论的物联网边缘计算资源优化机制[J]. 物联网学报, 2021,5(2):60-70. DOI： 10.11959/j.issn.2096-3750.2021.00225.

QI ZHANG, YUNA JIANG, XIAOHU GE, et al. Resource allocation based on optimal transport theory in IoT edge computing. [J]. Chinese journal on internet of things, 2021, 5(2): 60-70. DOI： 10.11959/j.issn.2096-3750.2021.00225.

摘要

随着物联网和边缘计算的发展，物联网设备可以将计算密集型任务卸载到边缘计算服务器上进行处理。由于物联网设备分布以及计算需求的变化，需要对边缘计算资源进行动态管理。利用最优运输理论对物联网中计算资源分配进行优化，提出一种基于物联网设备分布和边缘计算服务器位置的区域优化划分机制，在边缘计算服务器计算能力的约束下对物联网设备的能耗以及时延性能进行优化。仿真结果表明，与传统泰森多边形划分机制相比，该优化机制有更好的均衡性，并且物联网设备的平均能耗最多降低21%，平均时延最多降低45%。

Abstract

With the development of the Internet of things (IoT) and edge computing

the computation-intensive tasks of IoT devices can be offloaded to edge devices and processed at the edge of networks.Due to the variation of the distribution and computation requirements of IoT devices

the computation resources of edge networks need to be managed dynamically.The optimal transport theory was adopted to optimize the computation resources allocation in IoT networks.An optimized regional partition mechanism was proposed based on the distribution of IoT devices and locations of edge computing devices.Under constraints on the computing capabilities of edge computing devices

the energy consumption and delay of IoT devices were optimized.The simulation results show that

compared with the traditional Voronoi partition scheme

the proposed optimization mechanism shows better balance.The average transmitting power can be reduced by 21% and the average delay can be reduced by 45%.

关键词

物联网边缘计算资源分配最优运输理论能耗时延

Keywords

Internet of thingsedge computingresource allocationoptimal transport theoryenergy consumptiondelay

references

SHARMA S K, WANG X B . Live data analytics with collaborative edge and cloud processing in wireless IoT networks[J]. IEEE Access, 2017(5): 4621-4635.

JIANG Y N, GE X H, ZHONG Y ,et al. A new small-world IoT routing mechanism based on cayley graphs[J]. IEEE Internet of Things Journal, 2019,6(6): 10384-10395.

LIU H, ELDARRAT F, ALQAHTANI H ,et al. Mobile edge cloud system:architectures,challenges,and approaches[J]. IEEE Systems Journal, 2018,12(3): 2495-2508.

ZHANG W W, WEN Y G, GUAN K ,et al. Energy-optimal mobile cloud computing under stochastic wireless channel[J]. IEEE Transactions on Wireless Communications, 2013,12(9): 4569-4581.

YOU C S, HUANG K B, CHAE H ,et al. Energy-efficient resource allocation for mobile-edge computation offloading[J]. IEEE Transactions on Wireless Communications, 2017,16(3): 1397-1411.

CHEN X, JIAO L, LI W Z ,et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016,24(5): 2795-2808.

SARDELLITTI S, SCUTARI G, BARBAROSSA S . Joint optimization of radio and computational resources for multicell mobile-edge computing[J]. IEEE Transactions on Signal and Information Processing Over Networks, 2015,1(2): 89-103.

ZHAO T C, ZHOU S, GUO X Y ,et al. A cooperative scheduling scheme of local cloud and Internet cloud for delay-aware mobile cloud computing[C]// Proceedings of 2015 IEEE Globecom Workshops (GC Wkshps). Piscataway:IEEE Press, 2015: 1-6.

LIU J, MAO Y Y, ZHANG J ,et al. Delay-optimal computation task scheduling for mobile-edge computing systems[C]// Proceedings of 2016 IEEE International Symposium on Information Theory (ISIT). Piscataway:IEEE Press, 2016: 1451-1455.

XU J L, PALANISAMY B, LUDWIG H ,et al. Zenith:utility-aware resource allocation for edge computing[C]// Proceedings of 2017 IEEE International Conference on Edge Computing (EDGE). Piscataway:IEEE Press, 2017: 47-54.

KWAK J, KIM Y, LEE J ,et al. DREAM:dynamic resource and task allocation for energy minimization in mobile cloud systems[J]. IEEE Journal on Selected Areas in Communications, 2015,33(12): 2510-2523.

JIANG Z F, MAO S W . Energy delay trade-off in cloud offloading for mutli-core mobile devices[C]// Proceedings of 2015 IEEE Global Communications Conference (GLOBECOM). Piscataway:IEEE Press, 2015: 1-6.

MUÑOZ O, PASCUAL-ISERTE A, VIDAL J . Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading[J]. IEEE Transactions on Vehicular Technology, 2015,64(10): 4738-4755.

ZHAO J H, LI Q P, GONG Y ,et al. Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2019,68(8): 7944-7956.

DENG R L, LU R X, LAI C Z ,et al. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption[J]. IEEE Internet of Things Journal, 2016,3(6): 1171-1181.

CHEN X F, ZHANG H G, WU C ,et al. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning[J]. IEEE Internet of Things Journal, 2019,6(3): 4005-4018.

CHENG N, LYU F, QUAN W ,et al. Space/aerial-assisted computing offloading for IoT applications:a learning-based approach[J]. IEEE Journal on Selected Areas in Communications, 2019,37(5): 1117-1129.

WANG J D, ZHAO L, LIU J J ,et al. Smart resource allocation for mobile edge computing:a deep reinforcement learning approach[J]. IEEE Transactions on Emerging Topics in Computing, 2661(99): 1.

VILLANI C . Topics in optimal transportation[M]. Providence,Rhode Island: American Mathematical Society, 2003.

SANTAMBROGIO F . Optimal transport for applied mathematicians[M]. Cham: Springer International Publishing, 2015.

AMBROSIO L, GIGLI N . A user's guide to optimal transport[M]. Berlin Heidelberg: Springer, 2013.

GHAZZAI H . Environment aware cellular networks[D]. Thuwal:King Abdullah University of Science and Technology, 2015.

GHAZZAI H, TEMBINE H, ALOUINI M S . Mobile user association for heterogeneous networks using optimal transport theory[C]// Proceedings of 2017 6th International Conference on Communications and Networking (ComNet). Piscataway:IEEE Press, 2017: 1-6.

WANG Y, HU Z Q, WEN X M ,et al. Three-dimensional aerial cell partitioning based on optimal transport theory[C]// Proceedings of 2020 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway:IEEE Press, 2020: 1-6.

MOZAFFARI M, SAAD W, BENNIS M ,et al. Optimal transport theory for power-efficient deployment of unmanned aerial vehicles[C]// Proceedings of 2016 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2016: 1-6.

MOZAFFARI M, SAAD W, BENNIS M ,et al. Wireless communication using unmanned aerial vehicles (UAVs):optimal transport theory for hover time optimization[J]. IEEE Transactions on Wireless Communications, 2017,16(12): 8052-8066.

GE X H, YANG B, YE J L ,et al. Spatial spectrum and energy efficiency of random cellular networks[J]. IEEE Transactions on Communications, 2015,63(3): 1019-1030.

GE X H, YE J L, YANG Y ,et al. User mobility evaluation for 5G small cell networks based on individual mobility model[J]. IEEE Journal on Selected Areas in Communications, 2016,34(3): 528-541.

GE X H, TU S, MAO G Q ,et al. 5G ultra-dense cellular networks[J]. IEEE Wireless Communications, 2016,23(1): 72-79.

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