With the development of multiaccess edge computing (also called mobile-edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired Quality of Service (QoS). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to limited resources, heterogeneous servers, and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies of the deployment problem in edge environment consider such property. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work first builds a redundant service deployment model under a heterogeneous edge environment and defines it as a multiobjective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and the K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.

中文翻译：

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边缘计算环境中冗余服务的预算约束优化部署


随着多接入边缘计算（也称为移动边缘计算，MEC）的发展，越来越多的基于服务的应用程序被部署到边缘服务器，以确保所需的服务质量（QoS）。在边缘环境中，由于资源有限、服务器异构、用户地理位置不同，如何合理部署应用服务成为一个具有挑战性的问题。受益于其可重用性，单个服务可以由多个应用程序使用。然而，只有少数关于边缘环境中部署问题的研究考虑了这种特性。这项工作考虑了不同应用程序重用服务的冗余部署，从而实现高QoS。由于成本对于提供商来说很重要，其目标是在部署预算的约束下最小化传输成本和网络延迟。这项工作首先构建了异构边缘环境下的冗余服务部署模型，并将其定义为给定预算约束下的多目标优化问题。然后，计算服务优先级以确定冗余，并使用基于请求频率过滤的K-medoids聚类算法进行边缘服务器选择。接下来提出了一种基于优先级的遗传算法来获得优化计划。最后，这项工作在现实世界的数据集上进行了实验，以证明所提出的方法相对于现有方法的优越性。