C-V2X车联网中基于模拟退火算法的任务卸载与资源分配

Task offloading and resource allocation based on simulated annealing algorithm in C-V2X internet of vehicles

网联车辆节点产生的不同属性的大数据流量计算任务进行传输并卸载时,通常引起通信系统中时延抖动、计算能耗与系统开销大等问题,因此,根据实际通信环境,提出一种C-V2X车联网(IoV)中基于模拟退火算法(SAA)的任务卸载与资源分配方案。首先,根据任务处理优先程度,对处理优先程度较高的任务进行协同卸载计算处理;其次,通过全局搜索最优卸载比例因子的方式,制定了一种基于SAA的任务卸载策略,且分析并优化了任务卸载比例因子;最后,在任务卸载比例因子更新过程中,将系统开销最小化问题转化为功率和计算资源分配凸优化问题,并利用拉格朗日乘子法获取最优解。通过对所提算法与本地卸载、自适应遗传算法等作比较可知,随着计算任务的数据量不断增加,自适应遗传算法比本地卸载的时延、能耗、系统开销分别降低了5.97%、49.40%、49.36%,在此基础上基于SAA的方案较自适应遗传算法的时延、能耗、系统开销再降低了6.35%、92.27%、91.7%;随着计算任务CPU周期数不断增加,自适应遗传算法比本地卸载的时延、能耗、系统开销分别降低了16.4%、49.58%、49.23%,在此基础上基于SAA的方案较自适应遗传算法的时延、能耗、系统开销再降低了19.61%、94.39%、89.88%。实验结果表明,SAA不仅能降低通信系统时延、能耗及系统开销,还可以使结果加速收敛。

When big data flow calculation tasks with different attributes generated by networked vehicle nodes are transmitted and offloaded, issues such as time delay jitter, large computational energy consumption and system overhead usually happen. Therefore, according to the actual communication environment, a scheme for task offloading and resource allocation based on Simulated Annealing Algorithm(SAA) in Cellular Vehicle to Everything(C-V2X) Internet of Vehicles(IoV) was proposed. Firstly, according to the task processing priority, the tasks with high processing priority were processed by collaborative offloading and computing. Secondly, an SAA-based task offloading strategy was developed with the aid of globally searching for the optimal offloading scale factor. And the task offloading scale factor was analyzed and optimized.Finally, during the update process of task offloading scale factor, the problem of minimizing the system overhead was transformed into the convex optimization problem of power and computational resource allocation. And the Lagrange multiplier method was used to obtain the optimal solution. By comparing the proposed algorithm with the local offloading and adaptive genetic algorithm, it can be seen that: as the calculation task data size increases, the time delay, power consumption and system overhead of the adaptive genetic algorithm are decreased by 5. 97%, 49. 40%, and 49. 36%respectively, compared with those of the local offloading. On this basis, the time delay, power consumption and system overhead of the proposed SAA-based scheme are further decreased by 6. 35%, 92. 27%, and 91. 7% respectively, compared with those of the adaptive genetic algorithm. As the CPU cycles of the calculation task increase, the time delay, power consumption and system overhead of the adaptive genetic algorithm are decreased by 16. 4%, 49. 58%, and 49. 23%respectively, compared with local offloading. On this basis, the time delay, power consumption and system overhead of the proposed SAA-based scheme are further decreased by 19. 61%, 94. 39%, and 89. 88% respectively, compared with those of the adaptive genetic algorithm. Experimental results show that SAA cannot only reduce the time delay, power consumption and system overhead of communication systems but also accelerate convergence of the results.