A decentralized collaborative approach to online edge user allocation in edge computing environments

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.