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PDMA: Probabilistic Service Migration Approach for Delay-aware and Mobility-aware Mobile Edge Computing
arXiv - CS - Distributed, Parallel, and Cluster Computing Pub Date : 2021-06-10 , DOI: arxiv-2106.05584
Minxian Xu, Qiheng Zhou, Huaming Wu, Weiwei Lin, Kejiang Ye, Chengzhong Xu

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.

中文翻译:

PDMA:延迟感知和移动感知移动边缘计算的概率服务迁移方法

作为5G时代的一项关键技术,移动边缘计算(MEC)近年来发展迅速。MEC旨在降低移动用户的服务延迟,同时缓解核心网的处理压力。MEC可以看作是云计算在用户端的延伸,可以部署边缘服务器,让计算资源更贴近移动用户,提供更高效的交互。但是,由于用户的动态移动性,用户与边缘服务器之间的距离会动态变化,这可能会导致服务质量(QoS)的波动。因此,当移动用户在MEC环境中移动时,需要通过一定的方式对部署在边缘服务器上的业务进行调度,以保证用户体验。在本文中,我们在 MEC 场景中对服务调度进行建模,并基于简洁的概率方法提出了一种延迟感知和移动性感知的服务管理方法。这种方法计算复杂度低,可以有效降低服务延迟和迁移成本。此外,我们通过利用多个现实数据集进行实验,并使用 iFogSim 来评估算法的性能。结果表明,我们提出的方法可以优化服务延迟的性能,与高峰时段的基线相比,改进了 8% 到 20%,并将迁移成本降低了 75% 以上。我们利用多个现实数据集进行实验,并使用 iFogSim 来评估算法的性能。结果表明,我们提出的方法可以优化服务延迟的性能,与高峰时段的基线相比,改进了 8% 到 20%,并将迁移成本降低了 75% 以上。我们利用多个现实数据集进行实验,并使用 iFogSim 来评估算法的性能。结果表明,我们提出的方法可以优化服务延迟的性能,与高峰时段的基线相比,改进了 8% 到 20%,并将迁移成本降低了 75% 以上。
更新日期:2021-06-11
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