计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 231000080-7.doi: 10.11896/jsjkx.231000080
李文旺, 周浩浩, 邓苏, 马武彬, 吴亚辉
LI Wenwang, ZHOU Haohao, DENG Su, MA Wubin, WU Yahui
摘要： 车联网(IoV)与联网自动驾驶汽车(CAV)的结合推动了自动驾驶技术的飞速发展,但也带来了对计算资源的巨大需求,给资源受限的车辆带来了挑战。车辆边缘计算(VEC)的出现,提供了一种全新的解决方案,通过将任务卸载到路侧单元中的边缘服务器上,能够以更高效的方式为车联网提供服务。然而,多个车辆同时发出卸载请求时会产生资源抢占,增大任务处理延时,如何高效调度资源以最大化服务质量是一个亟待解决的问题。为此,文中旨在从多目标优化的角度,详细分析VEC计算卸载的延迟和能耗,使延迟和成本最小化,并提出了名为NSGA2TO的基于改进非支配排序遗传算法的任务卸载算法。该算法能够寻找出多目标优化问题的Pareto最优解,大量仿真结果验证了NSGA2TO的优越性能。此外,还探究了Pareto最优解所涉及的延迟与能耗之间的关系,有助于更好地理解车辆任务卸载问题的复杂性。通过合理平衡延迟和能耗,将能够进一步提升车联网系统的性能和效率,为用户提供更安全、更便捷的出行体验。
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