Skip to main content

Advertisement

Springer Nature Link
Log in

Synthetic Hardware Performance Analysis in Virtualized Cloud Environment for Healthcare Organization

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The main obstacles in mass adoption of cloud computing for database operations in healthcare organization are the data security and privacy issues. In this paper, it is shown that IT services particularly in hardware performance evaluation in virtual machine can be accomplished effectively without IT personnel gaining access to actual data for diagnostic and remediation purposes. The proposed mechanisms utilized the hypothetical data from TPC-H benchmark, to achieve 2 objectives. First, the underlying hardware performance and consistency is monitored via a control system, which is constructed using TPC-H queries. Second, the mechanism to construct stress-testing scenario is envisaged in the host, using a single or combination of TPC-H queries, so that the resource threshold point can be verified, if the virtual machine is still capable of serving critical transactions at this constraining juncture. This threshold point uses server run queue size as input parameter, and it serves 2 purposes: It provides the boundary threshold to the control system, so that periodic learning of the synthetic data sets for performance evaluation does not reach the host’s constraint level. Secondly, when the host undergoes hardware change, stress-testing scenarios are simulated in the host by loading up to this resource threshold level, for subsequent response time verification from real and critical transactions.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Foster, I., Zhao, Y., Raicu, I., and Lu, S.Y., Cloud Computing and Grid Computing 360-Degree Compared. Grid Comput. Environ. Workshop: 1–10, 2008.

  2. Cui, L. Z., Zhang, T. T., Xu, G. Q., and Yuan, D., A scheduling algorithm for multi-tenants instance-intensive workflows. Appl. Math. Inf. Sci. 7(1):99–105, 2013.

    Article  MathSciNet  Google Scholar 

  3. Chen, W., Yin, K. C., Yang, D. L., and Hung, M. C., Data migration from grid to cloud computing. Appl. Math. Inf. Sci. 7(1):399–406, 2013.

    Article  Google Scholar 

  4. Babar, M.A., and Chauhan, M.A., A tale of migration to cloud computing for sharing experiences and observations. In: Proceeding of the 2nd International Workshop on Software Engineering for Cloud Computing. pp 50–56, 2011.

  5. Kumar, V., and Garg, K. K., Migration of services to the cloud environment: Challenges and best practices. Int. J. Comput. Appl. 55(1):1, 2012.

    Google Scholar 

  6. Anonymous, TPC Benchmark, revision 2.14.4, 2012.

  7. Vishwanath, K., and Nagappan, N., Characterizing Cloud Computing Hardware Reliability. In: Proceedings of the 1st ACM symposium on Cloud computing. pp 193–204, 2010.

  8. Bichler, M., and Setzer, T., Admission control for media on demand services. J. Serv. Orient. Comput. Appl. 1(1):65–73, 2007.

    Article  Google Scholar 

  9. Neter, J., Kutner, M., Nachtsheim, C., and Wasserman, W., Applied Linear Regression Models. The McGraw-Hill Companies, Inc., USA, pp 4–36, 1996.

  10. Khatua, S., Ghosh, A., and Mukherjee, N., Optimizing the utilization of virtual resources in Cloud environment. In: 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems. pp 82–87, 2010.

  11. Ghoshal, D., Canon, R. S., and Ramakrishnan, L., I/O Performance of Virtualized Cloud Environments. In: In Proceeding of the 2nd International Workshop on Data Intensive Computing in the Clouds. pp 71–80, 2011.

  12. Wang, X. W., Sun, J. J., Li, H. X., Wu, C., and Huang, M., A reverse auction based allocation mechanism in the cloud computing environment. Appl. Math. Inf. Sci. 7(1):75–84, 2013.

    Article  Google Scholar 

  13. Xhafa, F., and Abraham, A., Computational models and heuristic methods for Grid scheduling problems. Futur. Gener. Comput. Syst. 26(4):608–621, 2010.

    Article  Google Scholar 

  14. Li, Q., and Guo, Y., Optimization of resource scheduling in cloud computing. In: 12th International Symposium on Symbolic and Numeric Algorithms for Scientific. Computing 2010:315–320, 2010.

    Google Scholar 

  15. Beloglazov, A., Abawajy, J., and Buyya, R., Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Futur. Gener. Comput. Syst. 28(5):755–768, 2012.

    Article  Google Scholar 

  16. Beloglazov, A., and Buyya, R., Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 2012 (PrePrint).

  17. Andrzejak, A., and Silva, L., Deterministic models of software aging and optimal rejuvenation schedules. In: IEEE International Symposium on Integrated Network Management. pp 159-168, 2007.

  18. Vaidyanathan, K., and Trivedi, K. S., A comprehensive model for software rejuvenation. IEEE Trans. Dependable Secure Comput. 2(2):124–137, 2005.

    Google Scholar 

  19. Lupse, O., Vida, M., and Stoicu-Tivadar, L., Cloud Computing and Interoperability in Healthcare Information Systems. In: The First International Conference on Intelligent Systems and Applications. pp 81–85, 2012.

  20. Ahuja, S., Mani, S., and Zambrano, J., A survey of the state of cloud computing in healthcare. Netw. Commun. Technol. 1(2):12–19, 2012.

    Google Scholar 

  21. Kumar, V., Swetha, M., Muneshwara, M. S., and Prakash, S., Cloud Computing: Towards case study of data security mechanism. Int. J. Adv. Technol. Eng. Res. 2(4), 2012.

  22. Padhy, R., Patra, M., and Satapathy, S., Design and implementation of a cloud based rural healthcare information system model. Univ. J. Appl. Comput. Sci. Technol. 2(1):149–157, 2012.

    Google Scholar 

  23. Hamlen, K., Kantarcioglu, K., Khan, L., and Thuraisingham, B., Security issues for cloud computing. Int. J. Inf. Secur. Priv. 4(2):39–51, 2010.

    Article  Google Scholar 

  24. Zhang, R., and Liu, L., Security models and requirements for healthcare application clouds. In: IEEE 3rd International Conference on Cloud. Computing 2010:268–275, 2010.

    Google Scholar 

  25. Donahue, S., Can cloud computing help Fix health care. Cloudbook J. 1(6), 2010.

  26. VMWare, VMware Infrastructure Architecture Overview. VMWare Inc., 2006.

  27. Tan, C. H., and Teh, Y. W., Harnessing cloud computing for dynamic resource requirement by Database Workloads. J. Inf. Sci. Eng. 29(5), 2013 (PrePrint).

  28. Mohri, M., Rostamizadeh, A., and Talwalkar, A., Foundations of Machine Learning. MIT Press, MA, USA, 2012.

  29. Banga, G., and Druschel, P., Measuring the Capacity of a Web Server. In: Proceeding of the USENIX Symposium on Internet Technologies & Systems, pp 61–71, 1997.

  30. Mosberger, D., and Jin, T., httperfm A Tool for Measuring Web Server Performance. ACM SIGMETRICS Performance Evaluation. 26(3):31–37, 1998.

    Google Scholar 

  31. Allen, A. O., Probability, statistics, and queuing theory with computer science applications, 2nd edition. Academic Press, Inc., San Diego, CA, USA, 1990.

    Google Scholar 

  32. Little, J. D. C., A proof for the queuing formula: L = ?W. Oper. Res. 9(3):383–387, 1961.

    Article  MathSciNet  Google Scholar 

  33. Zadeh, L. A., Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2):103–111, 1996.

    Google Scholar 

  34. Kleinbaum, D. G., Kupper, L. L., Muller, K. E., and Nizam, A., Applied regression analysis and other multivariable methods. Duxbury Press, Pacific Grove, CA, USA, 1998.

    Google Scholar 

  35. Hanmer, R., Software Rejuvenation. Alcatel-Lucent, 2010.

Download references

Conflict of interest

The authors declare that there is no conflict of interest in publishing this manuscript.

Author information

Authors and Affiliations

  1. Faculty of Computer Science & Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chee-Heng Tan & Ying-Wah Teh

Authors
  1. Chee-Heng Tan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ying-Wah Teh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chee-Heng Tan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tan, CH., Teh, YW. Synthetic Hardware Performance Analysis in Virtualized Cloud Environment for Healthcare Organization. J Med Syst 37, 9950 (2013). https://doi.org/10.1007/s10916-013-9950-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-013-9950-7

Keywords