Multi-objective task unloading algorithm oriented to vehicle edge computing
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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.
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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 
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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
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