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JACIII Vol.22 No.3 pp. 295-305
doi: 10.20965/jaciii.2018.p0295
(2018)

Paper:

Clustering-Based Cloud Migration Strategies

Mubeen Aslam, Lukman bin AB Rahim, Junzo Watada, and Manzoor Hashmani

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS
32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

Received:
January 19, 2018
Accepted:
February 8, 2018
Published:
May 20, 2018
Keywords:
cloud migration, legacy application, migration strategies, migration strategy selection process
Abstract

The k-means algorithm of the partitioning clustering method is used to analyze cloud migration strategies in this study. The extent of assistance required to be provided to organizations while working on migration strategies was investigated for each cloud service model and concrete clusters were formed. This investigation is intended to aid cloud consumers in selecting their required cloud migration strategy. It is not easy for businessmen to select the most appropriate cloud migration strategy, and therefore, we proposed a suitable model to solve this problem. This model comprises a web of migration strategies, which provides an unambiguous visualization of the selected migration strategy. The cloud migration strategy targets the technical aspects linked with cloud facilities and measures the critical realization factors for cloud acceptance. Based on similar features, a correlation among the migration strategies is suggested, and three main clusters are formed accordingly. This helps to link the cloud migration strategies across the cloud service models (software as a service, platform as a service, and infrastructure as a service). This correlation was justified using the digital logic approach. This study is useful for the academia and industry as the proposed migration strategy selection process aids cloud consumers in efficiently selecting a cloud migration strategy for their legacy applications.

Cite this article as:
M. Aslam, L. bin AB Rahim, J. Watada, and M. Hashmani, “Clustering-Based Cloud Migration Strategies,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.3, pp. 295-305, 2018.
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References
  1. [1] M. Bakery and R. Buyyaz, “Cluster computing at a glance,” High Performance Cluster Computing: Architectures and Systems, Vol.1, pp. 3-47, 1999.
  2. [2] C. S. Yeo, R. Buyya, H. Pourreza, R. Eskicioglu, P. Graham, and F. Sommers, “Cluster computing: High-performance, high-availability, and high-throughput processing on a network of computers,” Handbook of Nature-Inspired and Innovative Computing, pp. 521-551, 2006.
  3. [3] S. M. Hashemi and A. K. Bardsiri, “Cloud computing Vs. grid computing,” ARPN J. of Systems and Software, Vol.2, pp. 188-194, 2012.
  4. [4] H. Jin, “Challenges of grid computing,” Int. Conf. on Web-Age Information Management, pp. 25-31, 2005.
  5. [5] P. Mell and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology Special Publication 800-145, 2011.
  6. [6] A. Huth and J. Cebula, “The basics of cloud computing,” United States Computer Emergency Readiness Team, 2011.
  7. [7] C. C. Rao and M. L. Y. R. Kumar, “Cloud: computing services and deployment models,” Int. J. of Engineering and Computer Science, Vol.2, No.12, pp. 3389-3392, 2013.
  8. [8] G. Polančič, G. Jošt, and M. Heričko, “An experimental investigation comparing individual and collaborative work productivity when using desktop and cloud modeling tools,” Empirical Software Engineering, Vol.20, pp. 142-175, 2015.
  9. [9] S. Bhardwaj, L. Jain, and S. Jain, “Cloud computing: A study of infrastructure as a service (IAAS),” Int. J. of Information Technology and Web Engineering, No.2, Vol.1, pp. 60-63, 2010.
  10. [10] W. K. Assunção, R. E. Lopez-Herrejon, L. Linsbauer, S. R. Vergilio, and A. Egyed, “Reengineering legacy applications into software product lines: a systematic mapping,” Empirical Software Engineering, Vol.22, No.6, pp. 1-45, 2017.
  11. [11] R. Rai, G. Sahoo, and S. Mehfuz, “Exploring the factors influencing the cloud computing adoption: a systematic study on cloud migration,” SpringerPlus, Vol.4, p. 197, 2015.
  12. [12] W. Wu, W. Gentzsch, and J. Kern, “Dry-type transformer optimization using high performance cloud computing: Performance evaluation,” IEEE SoutheastCon, pp. 1-2, 2016.
  13. [13] W. Gentzsch, “How Cost Efficient is HPC in the Cloud? – A Cost Model for In-House Versus In-Cloud High Performance Computing,” https://community.theubercloud.com/cost-hpc-cloud/ [accessed February 2, 2014]
  14. [14] J. F. Zhao and J. T. Zhou, “Strategies and Methods for Cloud Migration,” Int. J. of Automation and Computing, Vol.11, pp. 143-152, 2015.
  15. [15] S. When, “Six Steps to Migration Project Success,” Applications: A White Paper Series, Syntel Inc., 2006.
  16. [16] T. Binz, F. Leymann, and D. Schumm, “CMotion: A Framework for Migration of Applications into and between Clouds,” 2011 IEEE Int. Conf. on Service-Oriented Computing and Applications (SOCA), pp. 1-4, 2011.
  17. [17] B. C. Tak and C. Tang, “Appcloak: Rapid migration of legacy applications into cloud,” 2014 IEEE 7th Int. Conf. onCloud Computing (CLOUD), pp. 810-817, 2014.
  18. [18] A. S. Ganesan and T. Chithralekha, “A Survey on Survey of Migration of Legacy Systems,” Proc. of the Int. Conf. on Informatics and Analytics, p. 72, 2016.
  19. [19] L. Rokach and O. Maimon, “Clustering methods,” Data Mining and Knowledge Discovery Handbook, pp. 321-352, 2005.
  20. [20] D. Sisodia, L. Singh, S. Sisodia, and K. Saxena, “Clustering Techniques: A Brief Survey of Different Clustering Algorithms,” Int. J. of Latest Trends in Engineering and Technology (IJLTET), Vol.1, pp. 82-87, 2012.
  21. [21] A. Saxena, M. Prasad, A. Gupta, N. Bharill, O. P. Patel, A. Tiwari, M. J. Er, W. Ding, and C.-T. Lin, “A review of clustering techniques and developments,” Neurocomputing, Vol.267, pp. 664-681, 2017.
  22. [22] V. Andrikopoulos, T. Binz, F. Leymann, and S. Strauch, “How to adapt applications for the Cloud environment,” Computing, Vol.95, pp. 493-535, 2013.
  23. [23] M. A. Babar, P. Lago, and A. V. Deursen, “Empirical research in software architecture: opportunities, challenges, and approaches,” Empirical Software Engineering, Vol.16, p. 539, 2011.
  24. [24] G. K. Juneja, “Use of Modeling Language to deploy applications in clouds,” Int. Workshop on Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA), pp. 58-59, 2012.
  25. [25] R. Harms and M. Yamartino, “The Economics of the Cloud White Paper,” Microsoft Whitepaper, Microsoft Corporation, 2010.
  26. [26] T. Garg, R. Kumar, and J. Singh, “A way to cloud computing basic to multitenant environment,” Int. J. of Advanced Research in Computer and Communication Engineering, Vol.2, pp. 2394-2399, 2013.
  27. [27] G. Kulkarni, R. Shelke, R. Palwe, P. Khatawkar, S. Bhuse, and H. Bankar, “Multi-tenant SaaS cloud,” 2013 4th Int. Conf. on Computing, Communications and Networking Technologies (ICCCNT), pp. 1-4, 2013.
  28. [28] S. Vogel and J. Draper-Rodi, “The importance of pilot studies, how to write them and what they mean,” Int. J. of Osteopathic Medicine, Vol.23, pp. 2-3, 2017.

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Last updated on Aug. 14, 2018