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JACIII Vol.20 No.6 pp. 928-940
doi: 10.20965/jaciii.2016.p0928
(2016)

Paper:

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Applying Intelligent Adaptation to Remote Cloud Datacenter Backup

Bao Rong Chang*, Hsiu-Fen Tsai**, and Cin-Long Guo*

*Department of Computer Science and Information Engineering, National University of Kaohsiung
700, Kaohsiung University Rd., Nanzih District, Kaohsiung 811, Taiwan

**Department of Marketing Management, Shu-Te University
59, Hun Shang Rd., Yen Chao, Kaohsiung 824, Taiwan

Received:
March 17, 2016
Accepted:
August 3, 2016
Published:
November 20, 2016
Creative Commons License
Keywords:
in-cloud NoSQL database, intelligent adaptation, remote datacenter backup, performance index, Thrift Java
Abstract
HBase and Cassandra are two most commonly used large-scale distributed NoSQL database management systems; especially applicable to a large amount of data processing. Regarding remote data backup, each kind of datacenter has its own backup strategy to prevent the risks of data loss. With Thrift Java, this paper aims to implement in-cloud high efficient remote datacenter backup applied to in-cloud NoSQL databases like HBase and Cassandra. The binary communications protocol technology from Apache Thrift is employed to establish the graphical user interface instead of the command line interface so as to ease data manipulation. In order to control the network traffic flow smoothly, intelligent adaptation using ANFIS and PSO is employed to tune the parameters of NoSQL databases during the remote data backup to improve QoS in the network. The stress test has taken on strictly data reading/writing and remote backup of a huge amount of data to verify the effectiveness. Finally, the performance evaluation of a variety of benchmark databases has been done by performance index. As a result, the proposed HBase approach outperforms the other databases.
Cite this article as:
B. Chang, H. Tsai, and C. Guo, “Applying Intelligent Adaptation to Remote Cloud Datacenter Backup,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 928-940, 2016.
Data files:
References
  1. [1] B. R. Chang, H.-F. Tsai, and C.-M. Chen, “Empirical Analysis of Server Consolidation and Desktop Virtualization in Cloud Computing,” Mathematical Problems in Engineering. Vol.2013, article ID 947234, p. 11, 2013.
  2. [2] B. R. Chang, H.-F. Tsai, C.-Y. Chen, and Y.-C. Tsai, “Assessment of In-Cloud Enterprise Resource Planning System Performed in a Virtual Cluster,” Mathematical Problems in Engineering, Vol.2014, Article ID 520534, p. 8, 2014.
  3. [3] B. R. Chang, H.-F. Tsai, C.-M. Chen, and C.-F. Huang, “Intelligent Adaptation for UEC Video/Voice over IP with Access Control,” Int. J. of Intelligent Information and Database Systems, Vol.8, No.1, pp. 64-80, 2014.
  4. [4] C.-Y. Chen, B. R. Chang, and P.-S. Huang, “Multimedia Augmented Reality Information System for Museum Guidance,” Personal and Ubiquitous Computing, Vol.18, No.2, pp. 315-322, 2014.
  5. [5] D. Carstoiu, E. Lepadatu, and M. Gaspar, “Hbase-non SQL Database, Performances Evaluation,” Int. J. of Advanced Computer Technology, Vol.2, No.5, pp. 42-52, 2010.
  6. [6] A. Lakshman and P. Malik, “Cassandra: A Decentralized Structured Storage System,” ACM SIGOPS Operating Systems Review, Vol.44, No.2, pp. 35-40, 2010.
  7. [7] N. O’Higgins, MongoDB and Python: Patterns and Processes for the Popular Document-Oriented Database, O’Reilly Media Inc., Sebastopol, CA, USA, 2011.
  8. [8] B. R. Chang, H.-F. Tsai, and C.-M. Chen, “Assessment of In-Cloud Enterprise Resource Planning System Performed in a Virtual Cluster,” Mathematical Problems in Engineering, Vol.2014, article ID 947234, p. 11, 2014.
  9. [9] J. Pokorny, “NoSQL Databases: A Step to Database Scalability in Web Environment,” Int. J. of Web Information Systems, Vol.9, No.1, pp. 69-82, 2013.
  10. [10] A. Giersch, Y. Robert, and F. Vivien, “Scheduling Tasks Sharing Files on Heterogeneous Master–Slave Platforms,” J. of Systems Architecture, Vol.52, No.2, pp. 88-104, 2006.
  11. [11] A. J. Chakravarti, G. Baumgartner, and M. Lauria, “The Organic Grid: Self-Organizing Computation on A Peer-to-Peer Network,” IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol.35, No.3, pp. 373-384, 2005.
  12. [12] L. George, HBase: the Definitive Guide. O’Reilly Media Inc., Sebastopol, CA, USA, 2011.
  13. [13] E. Okorafor and M. K. Patrick, “Availability of Jobtracker Machine in Hadoop/Mapreduce Zookeeper Coordinated Clusters,” Advanced Computing: An Int. J., Vol.3, No.3, pp. 19-30, 2012.
  14. [14] V. Parthasarathy, Learning Cassandra for Administrators, Packt Publishing Ltd., Birmingham, UK, 2013.
  15. [15] Y. Gu and R. L. Grossman, “Sector: A High Performance Wide area Community Data Storage and Sharing System,” Future Generation Computer Systems, Vol.26, No.5, pp. 720-728, 2010.
  16. [16] M. Slee, A. Agarwal, and M. Kwiatkowski, “Thrift: Scalable Cross-Language Services Implementation,” Facebook White Paper, Vol.5, p. 8, 2007.
  17. [17] J. J. Maver and P. Cappy, Essential Facebook Development: Build Successful Applications for the Facebook Platform, Addison-Wesley Professional, Boston, MA, USA, 2009.
  18. [18] R. Murthy and R. Goel, “Low-Latency Queries on Hive Warehouse Data. XRDS: Crossroads,” The ACM Magazine for Students, Vol.19, No.1, pp. 40-43, 2012.
  19. [19] A. C. Ramo, R. G. Diaz, and A. Tsaregorodtsev, “DIRAC RESTful API,” J. of Physics: Conference Series, Vol.396, No.5, ID: 052019, 2012.
  20. [20] R. Kuc, Apache Solr 4 Cookbook, Packt Publishing Co., Birmingham, UK, 2013.
  21. [21] J.-S. R. Jang, “ANFIS: Adaptive Network-Based Fuzzy Inference System,” IEEE Trans. on System, Man and Cybernetics, Vol.23, No.3, pp. 665-685, 1993.
  22. [22] H. M. I. Pousinho, V. M. F. Mendes, and J. P. S. Catalão, “A Hybrid PSO–ANFIS Approach for Short-Term Wind Power Prediction in Portugal,” Energy Conversion and Management, Vol.52, No.1, pp. 397-402, 2011.

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