QoS prediction for service recommendations in mobile edge computing

• Published in: Journal of parallel and distributed computing Vol. 127; pp. 134 - 144
• Main Authors: Wang, Shangguang, Zhao, Yali, Huang, Lin, Xu, Jinliang, Hsu, Ching-Hsien
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2019
• Subjects: Edge server similarity; Mobile edge computing; QoS; Service recommendation; Service recommendation; QoS; Edge server similarity; Mobile edge computing
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2017.09.014

Mobile edge computing is an emerging technology that provides services within the close proximity of mobile subscribers by edge servers that are deployed in each edge server. Mobile edge computing platform enables application developers and content providers to serve context-aware services (such as service recommendation) by using real time radio access network information. In service recommendation system, quality of service (QoS) prediction plays an important role when mobile devices or users want to invoke services that can satisfy user QoS requirements. However, user mobility (e.g., from one edge server to another) often makes service QoS prediction values deviate from actual values in traditional mobile networks. Unfortunately, many existing service recommendation approaches fail to consider user mobility. In this paper, we propose a service recommendation approach based on collaborative filtering and make QoS prediction based on user mobility. This approach initially calculates user or edge server similarity and selects the Top-K most-similar neighbors, predicts service QoS, and then makes service recommendation. We have implemented our proposed approach with experiments based on Shanghai Telecom datasets. Experimental results show that our approach can significantly improve on the accuracy of service recommendation in mobile edge computing. •A service recommendation approach based on collaborative filtering is proposed.•Our method takes advantages of both user mobility and data volatility to adapt to mobile edge computing environments.•Experimental results show that our approaches significantly improve the accuracy of service recommendation in mobile edge computing.