车联网云边协同计算场景下的多目标优化卸载决策

Multi-objective optimal offloading decision for cloud-edge collaborative computing scenario in Internet of vehicles

车联网场景下的计算任务对时延非常敏感,需要云边协同计算来满足这类需求。针对车联网云边协同计算场景下如何高效地进行服务卸载并同时考虑服务的卸载决策以及边缘服务器和云服务器的协同资源分配问题,设计了基于云边协同的车辆计算网络架构,在该架构下,车载终端、云服务器和边缘服务器都可以提供计算服务;通过对缓存任务进行分类并将缓存策略引入车联网场景,依次设计了缓存模型、时延模型、能耗模型、服务质量模型以及多目标优化问题模型;给出了一种基于改进的多目标优化免疫算法的卸载决策方案。最后,通过对比实验验证了所提卸载决策方案的有效性。

Computing tasks in Internet of vehicles are very sensitive to offloading delay, cloud-edge collaborative computing is required to meet such requirements. Aiming at the problem that in the cloud-edge collaborative computing scenario of the Internet of vehicles, it is a challenging problem that how to efficiently offload services, and simultaneously consider the offloading decisions of services with the collaborative resource allocation of edge servers and cloud servers, a vehicle computing network architecture based on cloud-edge collaboration was designed. In this architecture, vehicle terminals, cloud servers and edge servers could provide computing services. The cache strategy was introduced into the scenario of Internet of vehicles by classifying cache tasks. The cache model, delay model, energy consumption model,quality of service model and multi-objective optimization model were designed successively. An improved multi-objective optimization immune algorithm was proposed for offloading decision making. Finally, the effectiveness of the proposed offloading decision scheme was verified by comparative experiments.