Budget-Constrained Optimal Deployment of Redundant Services in Edge Computing Environment

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.

Automated summary

With the development of multiaccess edge computing, the deployment problem of application services in the edge environment becomes challenging due to limited resources, heterogeneous servers, and different geographical locations of users. This work focuses on the redundant deployment of reused services to achieve high QoS while minimizing transmission cost and network latency. It proposes a redundant service deployment model and formulates it as a multiobjective optimization problem under a given budget constraint. Genetic algorithm based on priority is utilized to obtain an optimized plan, which has been proven superior through experiments on real-world datasets.