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

Ruijuan Zheng,Junlong Zhu,Qingtao Wu,Li Yang,Ruoshui Liu,Mingchuan Zhang

doi:10.1155/2019/7430860

Ruijuan Zheng, Junlong Zhu + Show 4 more

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https://doi.org/10.1155/2019/7430860

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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.

Highlights

Millions of people are participating in Mobile Microlearning and the number of students who are enrolled in a single course at the same time can be as high as tens of thousands [1]
The core goal of Mobile Microlearning is to ensure that the users can receive all kinds of online learning resources provided by cloud platform without the limitation of time, space, and region
For the resource deployment framework proposed in this paper, we focus on the research of classification accuracy, because it is the primary condition for deploying resources to 2-tier cloud modules and modelling energy consumption

Summary

Introduction

Millions of people are participating in Mobile Microlearning and the number of students who are enrolled in a single course at the same time can be as high as tens of thousands [1]. It indicates that Mobile Microlearning is getting more prevalent. It will increase the load and the energy consumption of the cloud platform. The core goal of Mobile Microlearning is to ensure that the users (visitors) can receive all kinds of online learning resources provided by cloud platform without the limitation of time, space, and region. Current researchers focus on deploying the system to the existing cloud platform [2,3,4], user behaviour collection [5], framework and data analysis [6], learning style [7] or time management [8], and energy consumption in the Mobile Microlearning is rarely involved

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