广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 80-88.doi: 10.12052/gdutxb.230104
陈明秋, 黄家乐, 武继刚
Chen Ming-qiu, Huang Jia-le, Wu Ji-gang
摘要: 停驻车辆辅助边缘计算(Parked Vehicle Assisted Edge Computing, PVEC) 可充分利用停驻车辆闲置资源,有效缓解车载边缘计算中的资源供需矛盾。然而,因停车行为具有不确定性,车主可突然中止车辆对外提供的计算服务。这将增大用户获取可靠计算服务的难度。为此,本文提出一个面向PVEC的服务可靠增强问题,并引入任务复制技术,将该问题转化为任务副本卸载问题,目标是最小化任务副本的平均服务时延。同时,证明了该问题的NP难解性。为求解问题,提出一种贪婪算法(Greedy Algorithm, GA),优先为具有较大数据量的任务选取一组服务时延最小且满足其服务可靠需求的停驻车辆。此外,还提出一种增强遗传算法(Enhanced Genetic Algorithm, EGA),以进一步优化算法GA的解。实验结果表明,针对用户服务可靠需求变化的情形,与现有的冗余最小化算法及非任务复制算法相比,算法EGA和GA有效降低了任务副本的平均服务时延。
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