As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service (QoS). Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this paper, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8% to 20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.
翻译:作为5G时代的关键技术,移动边缘计算(MEC)近年来发展迅速。MEC的目的是减少移动用户的服务延迟,同时减轻核心网络的处理压力。MEC可以被视为用户方面云计算的一个延伸,用户方面可以部署边缘服务器,使计算资源更接近移动用户,提供更有效的互动。但是,由于用户的动态流动性,用户和边缘服务器之间的距离将发生动态变化,这可能导致服务质量的波动。因此,当移动用户在MEC环境中移动时,需要采用某些方法来安排在边缘服务器上部署的服务,以确保用户经验。在本文中,我们将服务安排在MEC情景中进行模型化,并提议以简明的概率方法为基础,采用延迟认知和流动性服务管理办法。这种方法的计算复杂性较低,可以有效减少服务延误和迁移成本。此外,我们通过利用多种现实的数据集和使用iFogSim来评估算法的性能,因此需要采用某些方法来安排在边端服务器上部署的服务,以确保用户经验。在本文件中,我们在MEC情景中模拟服务安排服务时间安排,并提议基于精确的方法,在20小时内以最精确地优化了服务效率,比20小时后,我们提议的迁移速度可以降低了20%的进度,比延延延延延延后延后延后延后延后延后延后延延后延延延后延延延后延后延后延后延后延后延后延后延后延后延后延后延后延。