An online machine learning-based content caching scheme in mobile edge computing networks

Abstract

Mobile edge computing (MEC) is an emerging and fast-growing distributed computing paradigm. It brings the computation and storage resources closer to mobile users while also processing data at the network edge to improve response time and save bandwidth. In tactical virtual training environments, latency is a key factor that affects training performance. Additionally, MEC provides both information service environment and cloud computing capabilities to enable real-time virtual training. Therefore, we designed a machine learning-based data caching and processing scheme for the virtual training networks. The design consists of three tiers, mobile devices, edge servers, and cloud servers, respectively. By pre-caching the critical content objects close to the mobile devices, our MEC network enables data transmission and processing at low latency. Utilizing machine learning techniques, our caching scheme can predict and select the content objects to be cached with optimal storage efficiency at network edge servers. Specifically, we decoupled the content caching problem into two subproblems, namely probability learning and content selection. For probability learning, the edge servers estimate the probability and frequency that each content object will be requested in the near future. The estimate is according to the content request pattern learned over time. For the content selection, the edge servers determine the content objects for caching to minimize the expected content delay with limited storage. To evaluate the performance of our proposed scheme, we developed a testbed with real mobile devices and servers. The experimental results validated the feasibility and significant performance gains of the proposed scheme.