Multi-objective task unloading algorithm oriented to vehicle edge computing
-
摘要: 为解决车联网动态环境下,计算和通信资源不足时的任务卸载问题,提出一种基于车辆边缘计算的多目标任务卸载算法。搭建车辆边缘计算中的通信模型和计算模型,考虑每个车辆应用的任务时延约束,设计了多目标优化目标函数,联合优化时延和能耗成本;引入交叉变异、非支配排序、拥挤度排序等技术,提出了多目标任务卸载算法。实验表明,相比于其他任务卸载算法,所提算法显著减少了处理任务的时间和能耗。Abstract: To solve the problem of task unloading when computing and communication resources are insufficient in the dynamic environment of Internet of vehicles, this paper proposed a multi-objective task unloading method oriented to vehicle edge computing. The paper established the communication model and computing model in vehicle edge computing, considered the task delay constraints of each vehicle application, designed a multi-objective optimization objective function, jointly optimized the delay and energy consumption costs, and proposed a multi-objective task unloading algorithm by introducing cross mutation, non-dominated sorting, congestion ranking and other technologies. The experiment shows that compared to other task offloading methods, the proposed method significantly reduces the processing time and energy consumption for tasks.
-
表 1 编码示例
卸载比例 $ {x}_{1}^{1} $ $ {x}_{1}^{2} $ $ {x}_{2}^{1} $ $ {x}_{2}^{2} $ $ {y}_{1}^{1} $ $ {y}_{2}^{1} $ $ {y}_{1}^{2} $ $ {y}_{2}^{2} $ 卸载比例值 0.40 0.13 0.05 0.17 0.20 0.55 0.11 0.22 表 2 实验参数
参数 值 $ \mathrm{车}\mathrm{辆}i\mathrm{的}\mathrm{传}\mathrm{输}\mathrm{功}\mathrm{率}{P}_{i} $ 40 dBm 任务$ {T}_{i} $的大小$ {d}_{i} $ 3~8 kB 任务$ {T}_{i} $所能接受的最大时间延迟$ {t}_{i}^{max} $ 2~5 ms 车辆$ i $本地的计算能力$ {f}_{i} $ 200~1000 MIPS 区域$ k $路边单元的计算能力$ {f}_{k}^{r} $ 800~2000 MIPS 区域$ k $基站的计算能力$ {f}_{k}^{b} $ 2000~4000 MIPS -
[1] Wang S X, Dey S. Adaptive mobile cloud computing to enable rich mobile multimedia applications[J]. IEEE Transactions on Multimedia, 2013, 15(4): 870-883. doi: 10.1109/TMM.2013.2240674 [2] 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. doi: 10.1109/MCOM.2017.1600863 [3] Chen C, Chen L L, Liu L, et al. Delay-optimized V2V-based computation offloading in urban vehicular edge computing and networks[J]. IEEE Access, 2020 (8): 18863-18873. doi: 10.1109/ACCESS.2020.2968465 [4] 张心宇,王 喆,郭 歌,等. 铁路信息系统云边协同体系架构研究[J]. 铁路计算机应用,2022,31(10):1-5. [5] 李 毅,董根才,蔺 伟,等. 边缘计算技术在铁路5G移动通信中的应用研究[J]. 中国铁路,2020(11):23-30. [6] Yu F X Q, Chen H P, Xu J Q. DMPO: Dynamic mobility-aware partial offloading in mobile edge computing[J]. Future Generation Computer Systems, 2018 (89): 722-735. doi: 10.1016/j.future.2018.07.032 [7] Qiao G H, Leng S P, Zhang K, et al. Collaborative task offloading in vehicular edge multi-access networks[J]. IEEE Communications Magazine, 2018, 56(8): 48-54. doi: 10.1109/MCOM.2018.1701130 [8] 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. [9] Mukherjee S, Shu L, Shah R. A survey of multi-access edge Computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6): 3903-3914. [10] Zhang K, Mao Y M, Leng S P, et al. Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading[J]. IEEE Vehicular Technology Magazine, 2017, 12(2): 36-44. doi: 10.1109/MVT.2017.2668838 [11] Sun Y L, Xu L, Tang Y L, et al. Traffic offloading for online video service in vehicular networks: a cooperative approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7630-7642. doi: 10.1109/TVT.2018.2837024 [12] Li M, Yu F R, Si P B, et al. Software-defined vehicular networks with caching and computing for delay-tolerant data traffic[C]//2018 IEEE International Conference on Communications (ICC), 20-24 May, 2018, Kansas City, USA. New York: IEEE, 2018: 1-6. [13] Mach P, Becvar Z. Mobile edge computing: a survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628-1656. [14] 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. doi: 10.1109/TNET.2015.2487344 [15] Chen C, Zeng Y N, Li H, et al. A multihop task offloading decision model in MEC-enabled internet of vehicles[J]. IEEE Internet of Things Journal, 2023, 10(4): 3215-3230. doi: 10.1109/JIOT.2022.3143529 [16] Zhao T C, Zhou S, Song L Q, et al. Energy-optimal and delay-bounded computation offloading in mobile edge computing with heterogeneous clouds[J]. China Communications, 2020, 17(5): 191-210. doi: 10.23919/JCC.2020.05.015 [17] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017 [18] Hossain M D, Khanal S, Huh E N. Efficient task offloading for MEC-enabled vehicular networks: a non-cooperative game theoretic approach[C]//2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), 17-20 August, 2021, Jeju Island, Korea. New York: IEEE, 2021: 11-16. [19] Abdullah S K, Jabir A J. A multi-objective task offloading optimization for vehicular fog computing[J]. Iraqi Journal of Science, 2022, 63(2): 785-800.