Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data
Chia-Lee Yang 1
,  
Chi-Yo Huang 2  
,  
Yu-Sheng Kao 3
,  
 
 
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1
National Center for High-Performance Computing, Hsinchu, Taiwan
2
Department of Industrial Education, National Taiwan Normal University, Taiwan
3
Department of Technology Management for Innovation, Graduate School of Engineering The University of Tokyo, Japan
4
Institute of Computer and Communication Engineering, National Cheng Kung University, Taiwan
CORRESPONDING AUTHOR
Chi-Yo Huang   

Department of Industrial Education, National Taiwan Normal University, No. 129, Heping East Road 1, 10610 Taipei, Taiwan
Online publish date: 2017-07-27
Publish date: 2017-07-27
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):4553–4589
KEYWORDS
TOPICS
ABSTRACT
The most dramatic factor shaping the future of higher education is Big Data and analytics. In the Big Data era, the explosive growth of massive data manipulations imposes a heavy burden on computation, storage, and communication in data centers. Increasing uncertainties in information system availability have become a daily serious problem. An appropriate evaluation and selection of the right information system disaster recovery (DR) site can ensure business continuity and investment optimization. Therefore, this research aims to establish an analytic framework for evaluating, selecting DR sites for academic Big Data. The proposed analytic framework is consisting of the Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based network process (DNP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. An empirical study based on a real Big Data DR application of an Asian high-performance computer center’s evaluation and selection of DR sites for academic Big Data will be used to illustrate the feasibility of the proposed framework. The analytic results can serve as a foundation for information technology (IT) administrators’ strategies to reduce the performance gaps of a DR site for Big Data manipulations in general, and academic Big Data manipulations in special.
 
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