A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Abstract

Mobile Microlearning, a novel fusion form of the mobile Internet, cloud computing, and microlearning, becomes more prevalent in recent years. However, its high deployment and operational costs make energy saving in cloud become a concerning issue. In this paper, to save energy consumption, a resource deployment approach to cloud service provision for Mobile Microlearning is proposed. Chinese Lexical Analysis System and Dynamic Term Frequency-Inverse Document Frequency (D-TF-IDF) are adopted to implement resource classification. Resources are deployed to the 2-tier cloud architecture according to the classification results. Grey Wolf Optimization (GWO) algorithm is used to forecast real-time energy consumption per byte. The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.