Human-centric storage resource mechanism for big data on cloud service architecture
- 488 Downloads
With the rapid advancement of information technology in recent years, significant research addressing the efficient storage of big data has been conducted. Traditionally, big data with media-driven service have simply implied extensive amounts of data. However, this definition has evolved to include the extraction of values, analysis, and the prediction of results from a vast volume of unstructured and varied datasets. Because of the explosive growth of computer processing technologies, the creation of big data has originated from unstructured data, text data, image data, and location data created by a variety of digital devices. Classically, the storage of big data has been administered by companies that provide storage services or by specialized storage companies. Significant cost is incurred to store big data efficiently and maintain sufficient storage requirements, which increase continuously. In this paper, a human-centric Resource-Integrated System for Big Data (RISBD) is proposed that utilizes the resources of legacy desktop computers for big data storage to future communication. This is advantageous in terms of the cost of implementing a new storage system. Furthermore, it provides high storage scalability because it is an XML-based standard storage integration system developed using software.
KeywordsHuman-centric resource management Big data storage Legacy desktop computer Resource-integrated mechanism Distributed file system Fault tolerance Media-driven service
This Research has been performed as a subproject of project Global Science experimental Data hub Center (GSDC) and supported by the KOREA INSTITUTE of SCIENCE and TECHNOLOGY INFORMATION (KISTI). And also this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2053564).
- 1.Jeong YS, Kim HW, Jang HJ (2013) Adaptive resource management scheme for monitoring of CPS. J Supercomput 66(1):57–69Google Scholar
- 2.Degefa FB, Won D (2013) Extended key management scheme for dynamic group in multi-cast communication. J Converg 4(4):7–13Google Scholar
- 3.Song EH, Kim HW, Jeong YS (2012) Visual monitoring system of multi-hosts behavior for trustworthiness with mobile cloud. J Inform Process Syst 8(2)Google Scholar
- 4.Lee SH, Lee IY (2013) A secure index management scheme for providing data sharing in cloud storage. J Inform Process Syst 9(2):287–300Google Scholar
- 5.Malkawi MI (2013) The art of software systems development: reliability, availability, maintainability, performance (RAMP). Hum Centric Comput Inform Sci 3(22):1–17Google Scholar
- 7.Shrivastava N, Kumar G (2013) A survey on cost effective multi-cloud storage in cloud computing. Int J Adv Res Comput Eng Technol 2(4)Google Scholar
- 8.Kim SY, Roh HC, Park CH, Park SH (2009) Analysis of metadata server on clustered file systems. In: Proceedings of the Korea Computer Congress, KCC, South KoreaGoogle Scholar
- 9.Bojewar S, Das JA, (2013) A survey: data storage technologies. Int J Eng Sci Innov Technol 2(2): 547–554Google Scholar
- 10.Gibson GA, Van Meter R (2000) Network attached storage architecture. Communications of the ACM 43(11):37–45Google Scholar
- 11.Zhang X, Feng X (2013) Survey of research on big data storage. In: Proceedings of the 12th distributed computing and applications to business, engineering and science, DCABES, pp 76–80Google Scholar