Skip to main content
Springer Nature Link
Log in

Human-centric storage resource mechanism for big data on cloud service architecture

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Jeong YS, Kim HW, Jang HJ (2013) Adaptive resource management scheme for monitoring of CPS. J Supercomput 66(1):57–69

  2. Degefa FB, Won D (2013) Extended key management scheme for dynamic group in multi-cast communication. J Converg 4(4):7–13

  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)

  4. Lee SH, Lee IY (2013) A secure index management scheme for providing data sharing in cloud storage. J Inform Process Syst 9(2):287–300

  5. Malkawi MI (2013) The art of software systems development: reliability, availability, maintainability, performance (RAMP). Hum Centric Comput Inform Sci 3(22):1–17

  6. USA 25th TODAY. http://usatoday30.usatoday.com/tech/top25-internet.htm?csp=34#open-share-help

  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)

  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 Korea

  9. Bojewar S, Das JA, (2013) A survey: data storage technologies. Int J Eng Sci Innov Technol 2(2): 547–554

  10. Gibson GA, Van Meter R (2000) Network attached storage architecture. Communications of the ACM 43(11):37–45

  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 76–80

  12. Dong B, Zheng Q, Tian F, Chao K, Ma R, Anane R (2012) An optimized approach for storing and accessing small files on cloud storage. J Netw Comput Appl 35(6):1847–1862

    Article  Google Scholar 

  13. Hadoop. http://hadoop.apache.org/docs/r0.23.10/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html

  14. DAS. http://www.ibm.com/developerworks/library/l-linux-storage/

  15. NAS. http://www.ibm.com/developerworks/library/l-linux-storage/

  16. SAN. http://www.ibm.com/developerworks/library/l-linux-storage/

Download references

Acknowledgments

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).

Author information

Authors and Affiliations

  1. Department of Multimedia Engineering, Dongguk University, Seoul, 100-715, Republic of Korea

    Hyun-Woo Kim & Young-Sik Jeong

  2. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, 139-743, Republic of Korea

    Jong Hyuk Park

Authors
  1. Hyun-Woo Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jong Hyuk Park
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Young-Sik Jeong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Young-Sik Jeong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, HW., Park, J.H. & Jeong, YS. Human-centric storage resource mechanism for big data on cloud service architecture. J Supercomput 72, 2437–2452 (2016). https://doi.org/10.1007/s11227-015-1390-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-015-1390-3

Keywords