Opportunistic Content Offloading for Mobile Edge Computing

Abstract

It has been envisioned that future Mobile Edge Computing (MEC) paradigm is enabled with cache ability. Considering some of the content requests are highly concentrated, the popular contents will be repeatedly requested. To prevent frequent extra content request that will burden network backhaul, Base station (BS) with cache ability and even mobile user with the exact content can provide flexible content offloading on-consume. In this paper, we propose an opportunistic content offloading scheme by predicting the opportunistic content providers among mobile users and edges for MEC paradigm. At first, we propose to predict the opportunistic mobile content providers with popular contents according to historical data record. We then propose the opportunistic content offloading algorithm modeled by Stackelberg game. During the process, we consider mobile content consumer and content providers including mobile users and MEC server (e.g., BS) as the relationship of “leader-followers” in Stackelberg game. Based on the prediction of opportunistic connection with neighboring content provides, we design an iterative algorithm to reach the optimal equilibrium pricing with fast convergence. Our simulations are based on the real dataset provided by China Mobile Communications Corporation. The simulation results show our scheme can efficiently alleviate the network backhaul. During the peak hours, the number of content unloaded by our method accounts for 34.5% of the original total content load, thus effectively reducing the content overload pressure of the BS.

This work was supported in part by the National Natural Science Foundation of China under Grant 61702387, in part by the National Key Research and Development Program under Grant 2017YFB0504103 and Grant 2017YFC0503801, in part by the Development Program of China (863 Program) under Grant 2014AA01A707, and in part by the Natural Science Foundation of Hubei Province of China under Grant 2017CFB302.