History-Assisted Online User Allocation in Mobile Edge Computing

Abstract

Mobile edge computing (MEC) is emerging as a novel computing paradigm that pushes network resources (such as computation and storage resources) away from the centralized data center to distributed edge servers. By hiring various resources of nearby edge servers, the MEC provides high-bandwidth and low-latency network services for mobile users. As numerous mobile users may compete for limited edge servers’ resources, to improve the resource utilization of the MEC system, it is very critical to investigate an effective user allocation policy. Previous studies mainly focus on investigating offline user allocation policies. However, mobile users may arrive online, and the MEC should be able to allocate these users online too. In a real-world MEC environment, online allocation decisions should not be made entirely in the dark. The historical user requests which may contain powerful hints about future user requests, can be adopted to assist in making allocation decisions. In this paper, we take the historical data into account and study the history-assisted online user allocation strategy. Specifically, we formulate the user allocation problem with a comprehensive model and show its hardness. Then, we present an online algorithm named HOUA to allocate mobile users according to both the online arrived user requests and the historical user requests. The competitive ratio of HOUA is proved. To further verify the effectiveness of HOUA, we conduct experiments on a widely-used real-world dataset. We show that HOUA can allocate more mobile users and achieve high resource rental revenue compared with the other approaches.