User Preference-Based Hierarchical Offloading for Collaborative Cloud-Edge Computing

Abstract

Cloud computing and mobile edge computing techniques supply efficient ways to solve the contradiction between the increasing computing and storage demands of portable terminals and the limited capacity. In this paper, we conduct a three-tier hierarchical service system with multiple mobile users(UEs), multiple mobile edge computing servers(MECs), and a single cloud center(CC). It is worth noting that multiple UEs with personalized options generate a large number of different tasks in real time. To deal with this complex offloading problem, a response ratio offloading strategy (RROS) centered on user preference and real-time nature is designed to make MECs or CC serve as many UEs as possible. Therefore, a MEC-choosing preference list of each UE is created based on its past experiences at first. Then, each MEC iteratively sorts UEs with its ranking in the UEs' preference list. In order to avoid that the first task arriving at MEC occupies too many resources of MEC and cannot achieve global optimization, we also adopt loop iterative sequencing for multiple tasks arriving within a stipulated time. Lastly, by comparing the optimal response ratio on different MECs and CC, multiple MECs and the CC collaborative offload computing tasks of multiple UEs. We demonstrate numerical examples and data of the proposed strategy. Experimental results show that the algorithm significantly outperforms conventional techniques even with the increase of users.