浏览全部资源
扫码关注微信
1. 深圳大学电子与信息工程学院,广东 深圳 518060
2. 鹏城实验室宽带通信研究部,广东 深圳 518066
[ "李贤(1988- ),男,博士,深圳大学电子与信息工程学院副研究员,主要研究方向为移动边缘计算、无线供电通信等系统的性能优化设计" ]
[ "毕宿志(1987- ),男,博士,深圳大学电子与信息工程学院副教授,主要研究方向为无线通信网络资源管理与调度优化" ]
[ "曾泓儒(1996- ),男,深圳大学电子与信息工程学院硕士生,主要研究方向为移动边缘计算系统的优化设计" ]
[ "林彬(1997- ),男,深圳大学电子与信息工程学院硕士生,主要研究方向为移动边缘计算系统的优化设计" ]
[ "林晓辉(1975- ),男,博士,深圳大学电子与信息工程学院教授,主要研究方向为无人机通信网络的优化设计" ]
纸质出版日期:2022-12-30,
网络出版日期:2022-12,
移动端阅览
李贤, 毕宿志, 曾泓儒, 等. 基于智能化用户协作的边缘计算任务卸载与资源分配优化[J]. 物联网学报, 2022,6(4):41-52.
XIAN LI, SUZHI BI, HONGRU ZENG, et al. Collaborative task offloading and resource allocation optimization for intelligent edge devices. [J]. Chinese journal on internet of things, 2022, 6(4): 41-52.
李贤, 毕宿志, 曾泓儒, 等. 基于智能化用户协作的边缘计算任务卸载与资源分配优化[J]. 物联网学报, 2022,6(4):41-52. DOI: 10.11959/j.issn.2096-3750.2022.00303.
XIAN LI, SUZHI BI, HONGRU ZENG, et al. Collaborative task offloading and resource allocation optimization for intelligent edge devices. [J]. Chinese journal on internet of things, 2022, 6(4): 41-52. DOI: 10.11959/j.issn.2096-3750.2022.00303.
为了解决移动边缘计算网络中计算资源日益紧缺的问题,设计了一种基于用户协作的边缘计算资源分配机制,充分利用用户之间的空闲计算资源,有效提升系统整体的数据处理性能。以最大化用户的效用函数为目标,将目标优化问题建模为一个关于用户任务卸载决策和本地计算通信资源的联合优化问题,并结合深度学习技术和凸优化理论,提出了一种混合深度学习-优化算法对目标问题进行求解。仿真结果表明,相较于对比算法,所提算法能使用户的效用提升至少85.4%,并能在亚秒级的时间内实现用户效用的近似最优化。
In order to deal with the increasingly scarce computing resources
a cooperative edge computing scheme was proposed
which makes full use of the idle resources among users to improve the overall data processing performance.To maximize the user utility
the target problem was formulated as an MINLP (mixed integer non-linear programming)
and a learning-optimization-integrated method was proposed to jointly optimize the resource allocation and user offloading decisions.Simulation results show that the proposed scheme can produce a near-optimal solution in sub-second and effectively improve the system utility at least 85.4% compared to the considered benchmark methods.
移动边缘计算效用最大化凸优化深度学习
mobile edge computingutility maximizationconvex optimizationreinforcement learning
GSMA Intelligence. Understanding 5G:perspectives on future technological advancements in mobile[EB]. 2014.
LIN P, SONG Q Y, YU F R ,et al. Wireless virtual reality in beyond 5G systems with the Internet of intelligence[J]. IEEE Wireless Communications, 2021,28(2): 70-77.
SONKOLY B, SZABÓ R, NÉMETH B ,et al. 5G applications from vision to reality:multi-operator orchestration[J]. IEEE Journal on Selected Areas in Communications, 2020,38(7): 1401-1416.
AGIWAL M, ROY A, SAXENA N . Next generation 5G wireless networks:a comprehensive survey[J]. IEEE Communications Surveys& Tutorials, 2016,18(3): 1617-1655.
WEN M W, LI Q, KIM K J ,et al. Private 5G networks:concepts,architectures,and research landscape[J]. IEEE Journal of Selected Topics in Signal Processing, 2022,16(1): 7-25.
ZENG J, SUN J Y, WU B W ,et al. Mobile edge communications,computing,and caching (MEC3) technology in the maritime communication network[J]. China Communications, 2020,17(5): 223-234.
MACH P, BECVAR Z . Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Communications Surveys &Tutorials, 2017,19(3): 1628-1656.
TRAN T X, HAJISAMI A, PANDEY P ,et al. Collaborative mobile edge computing in 5G networks:new paradigms,scenarios,and challenges[J]. IEEE Communications Magazine, 2017,55(4): 54-61.
LI X, HUANG L, WANG H ,et al. An integrated optimization-learning framework for online combinatorial computation offloading in MEC networks[J]. IEEE Wireless Communications, 2022,29(1): 170-177.
BAI T, PAN C H, HAN C ,et al. Reconfigurable intelligent surface aided mobile edge computing[J]. IEEE Wireless Communications, 2021,28(6): 80-86.
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.
MAO Y Y, ZHANG J, LETAIEF K B . Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016,34(12): 3590-3605.
YAN J, BI S Z, ZHANG Y J A . Offloading and resource allocation with general task graph in mobile edge computing:a deep reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, 2020,19(8): 5404-5419.
WANG F, XU J, WANG X ,et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2018,17(3): 1784-1797.
LI X, BI S Z, QUAN Z ,et al. Online cognitive data sensing and processing optimization in energy-harvesting edge computing systems[J]. IEEE Transactions on Wireless Communications, 2022,21(8): 6611-6626.
WANG Y T, SHENG M, WANG X J ,et al. Mobile-edge computing:partial computation offloading using dynamic voltage scaling[J]. IEEE Transactions on Communications, 2016,64(10): 4268-4282.
HE X Y, XING H, CHEN Y ,et al. Energy-efficient mobile-edge computation offloading for applications with shared data[C]// Proceedings of 2018 IEEE Global Communications Conference. Piscataway:IEEE Press, 2018: 1-6.
HU X Y, WONG K K, YANG K . Wireless powered cooperation-assisted mobile edge computing[J]. IEEE Transactions on Wireless Communications, 2018,17(4): 2375-2388.
HE B Q, BI S Z, XING H ,et al. Collaborative computation offloading in wireless powered mobile-edge computing systems[C]// Proceedings of 2019 IEEE Globecom Workshops. Piscataway:IEEE Press, 2019: 1-7.
YOU C S, HUANG K B . Exploiting non-causal CPU-state information for energy-efficient mobile cooperative computing[J]. IEEE Transactions on Wireless Communications, 2018,17(6): 4104-4117.
DONG X Q, LI X H, YUE X W ,et al. Performance analysis of cooperative NOMA based intelligent mobile edge computing system[J]. China Communications, 2020,17(8): 45-57.
ADHIKARI M, SRIRAMA S N, AMGOTH T . Application offloading strategy for hierarchical fog environment through swarm optimization[J]. IEEE Internet of Things Journal, 2020,7(5): 4317-4328.
BI S Z, 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.
FENG H, GUO S T, YANG L ,et al. Collaborative data caching and computation offloading for multi-service mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2021,70(9): 9408-9422.
YU S, DAB B, MOVAHEDI Z ,et al. A socially-aware hybrid computation offloading framework for multi-access edge computing[J]. IEEE Transactions on Mobile Computing, 2020,19(6): 1247-1259.
HE J Y, ZHANG D, ZHOU Y Z ,et al. A truthful online mechanism for collaborative computation offloading in mobile edge computing[J]. IEEE Transactions on Industrial Informatics, 2020,16(7): 4832-4841.
MAO Y Y, YOU C S, ZHANG J ,et al. A survey on mobile edge computing:the communication perspective[J]. IEEE Communications Surveys & Tutorials, 2017,19(4): 2322-2358.
NGUYEN T T, HA V N, LE L B ,et al. Joint data compression and computation offloading in hierarchical fog-cloud systems[J]. IEEE Transactions on Wireless Communications, 2020,19(1): 293-309.
BERTHOLD T . Heuristic algorithms in global MINLP solvers[D]. Berlin:Technical University of Berlin, 2014.
BOYD S, VANDENBERGHE L . Convex optimization[M]. Cambridge: Cambridge University Press, 2004.
MEHROTRA S . On the implementation of aprimal-dual interior point method[J]. SIAM Journal on Optimization, 1992,2(4): 575-601.
HUANG L, BI S Z, ZHANG Y J A . 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.
LIN B, LIN X H, ZHANG S L ,et al. Computation task scheduling and offloading optimization for collaborative mobile edge computing[C]// Proceedings of 2020 IEEE 26th International Conference on Parallel and Distributed Systems. Piscataway:IEEE Press, 2020: 728-734.
0
浏览量
517
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构