Challenges of Capacity Modelling in Complex IT Architectures

Andrea Kő, Péter Fehér, Zoltán Szabó

2014

Abstract

As internal cloud, and cloud technologies widespread among companies, the responsibility of providing reliable IT infrastructure and adequate capacities became the top priority for companies. While internal clouds and related technologies creates the flexibility for customer, limited IT resources arise problems for providing capacities, that has impact on IT service quality. The presented research addressing this problem, and seeks creating models describing the relationship between IT service quality and background infrastructure capacity usage with two distinct methodologies, in a complex cloud-like environment of a financial institution. The research was analysed a pilot area of a widely used electronic banking service. As multivariate statistical modelling and hypothesis testing had limited results in phase 1, but in phase 2 further modelling opportunities were explored, a model based neural networks were developed. The research analyses the limitations of pure statistical analysis in cloud-like environments, but concludes to the usability of alternative methods.

References

  1. Bagley, W.; Hanna, A.; Howels, V.; Rasmussen, K.; Rytkonnen, P.; Westover, J.; Yuhas, J., 2002. MOF Service Management Function Capacity Management. Microsoft Corporation, Redmond
  2. Barlow, Horace B., 1989. Unsupervised learning. Neural computation 1.3 pp. 295-311.
  3. Broussard, Frederick W., 2008. IT Service Management Needs and Adoption Trends: An Analysis of a Global Survey of IT Executives. IDC White Paper. http://whitepapers.techrepublic.com.com/abstract.aspx ?docid=395611 (accessed: 28.06.2009)
  4. Chen, Y., Iyer, S., Liu, X., Milojicic, D., Sahai, A., 2007. SLA Decomposition: Translating Service Level Objectives to System Level Thresholds. Enterprise Systems and Software Lab, HP Labs. http://www.hpl.hp.com/techreports/2007/HPL-2007- 17.pdf. (accessed: 28.12.2013)
  5. Ehikhamenor, F.A., 2003. Information technology in Nigerian banks: The limits of expectations. Information Technology for Development 10, 13-24.
  6. EMA, 2012. IT Optimization through Predictive Capacity Management. ENTERPRISE MANAGEMENT ASSOCIATES (EMA) White Paper. http://www.ca.com//media/Files/IndustryAnalystRep orts/it-optimization-through-predictive-capacitymanagement.pdf (accessed: 28.08.2012)
  7. Fung, M.K., 2008. To What Extent Are Labor-Saving Technologies Improving Efficiency in the Use of Human Resources? Evidence from the Banking Industry. Production and Operations Management 17, pp. 75-92.
  8. Heckmann, B., 2012. Service Quality and Profit Control in Utility Computing Service Life Cycles, Doctorate, University of Plymouth, Plymouth, http://hdl.handle.net/10026.1/1568 (accessed: 28.12.2013)
  9. Higday-Kalmanowitz C.; Simpson E. S. (eds.), 2004. Implementing Service and Support Management Processes: A Practical Guide. Van Haren Publishing
  10. HP, 2008. End-to-end service management in the virtualized environment. White Paper. https://h10078.www1.hp.com/bto/download/4AA2- 3182ENW.pdf (accessed: 28.08.2012)
  11. Kant, K.; Srinivasan, M. M., 1992. Introduction to computer system performance evaluation. McGrawHill International edition.
  12. Kousiouris, G.; Kyriazis, D.; Gogouvitis, S.; Menychtas, A.; Konstanteli, K.; Varvarigou, T., 2011. "Translation of application-level terms to resource-level attributes across the Cloud stack layers," Computers and Communications (ISCC), 2011 IEEE Symposium on, vol., no., pp.153,160, June 28 2011-July 1 2011
  13. Lee, J.W.; Asanovic, K., 2006. "METERG: MeasurementBased End-to-End Performance Estimation Technique in QoS-Capable Multiprocessors," Real-Time and Embedded Technology and Applications Symposium, 2006. Proceedings of the 12th IEEE , vol., no., pp.135,147, 04-07 April 2006
  14. Lee, M.-C., 2009. Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications 8, pp. 130-141.
  15. Liao, Z., Wong, W.K., 2007. The determinants of customer interactions with internet-enabled e-banking services. Journal of the Operational Research Society 59, pp. 1201-1210.
  16. Metzger, A., Chi-Hung C.; Engel, Y.; Marconi, A., 2012. Research challenges on online service quality prediction for proactive adaptation. In.: Proceedings Software Services and Systems Research - Results and Challenges (S-Cube), 2012 Workshop on European Software Services and Systems Research - Results and Challenges (S-Cube), pp. 51 - 57
  17. Metzler, J., 2003. The Mandate to Implement Unified Performance Management. Accessed: 29 Sept 2009, source: http://www.comnews.com/WhitePaper_Libra ry/Network_management/pdfs/NetScout_wp_Metzler_ Mandate_to_Implement_Unified_Performance_Manag ement.pdf
  18. Mohri, M., Rostamizadeh, A., Talwalkar, A., 2012. Foundations of Machine Learning. The MIT Press ISBN 9780262018258.
  19. OGC. 2007. ITIL Service Design. London: The Stationery Office Ltd.
  20. OGC, 2011. ITIL Service Design. London: The Stationery Office Ltd.
  21. Rexer, K., 2009. Data Mining Tools Used Poll. KDNuggets, http://www.kdnuggets.com/polls/2009/data-miningtools-used.htm (2009.08.24)
  22. Rountree, D., Castrillo, I. (2013) The Basics of Cloud Computing, Understanding the Fundamentals of Cloud Computing in Theory and Practice, Elsevier
  23. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986. „Learning internal representations by error propagation”. In Rumelhart, D. E. and McClelland, J. L. (eds.), „Parallel Distributed Processing”, Vol. 1., MIT Press, Cambridge, MA.
  24. Sifaoui, A., Abdelkrim, A., Benrejeb, M., 2008. On the Use of Neural Network as a Universal Approximator. International Journal on Sciences and Techniques of Automatic control & computer engineering, IJ-STA, Vol. 2, N. 1, pp. 386-399.
  25. Sitaram, D., Manjunath, G. 2011. Moving To The Cloud: Developing Apps in the New World of Cloud Computing. Elsevier
  26. Stephens, D., 2010. The Challenges Monitoring Composite Applications. MeasureIT 8.
  27. Taylor, S. 2007. ITIL: The official introduction to the ITIL Service Lifecycle. The Stationery Office Ltd.
  28. Turban, E., Sharda, R., Delen, D., 2011. Decision Support and Business Intelligence Systems. 9/E, (ISBN-10: 013610729X)
  29. Vatanasombut, B., Igbaria, M., Stylianou, A.C., Rodgers, W., 2008. Information systems continuance intention of web-based applications customers: The case of online banking. Information & Management 45, pp. 419-428.
Download


Paper Citation


in Harvard Style

Kő A., Fehér P. and Szabó Z. (2014). Challenges of Capacity Modelling in Complex IT Architectures . In Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-019-2, pages 543-550. DOI: 10.5220/0004851505430550


in Bibtex Style

@conference{closer14,
author={Andrea Kő and Péter Fehér and Zoltán Szabó},
title={Challenges of Capacity Modelling in Complex IT Architectures},
booktitle={Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2014},
pages={543-550},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004851505430550},
isbn={978-989-758-019-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Challenges of Capacity Modelling in Complex IT Architectures
SN - 978-989-758-019-2
AU - Kő A.
AU - Fehér P.
AU - Szabó Z.
PY - 2014
SP - 543
EP - 550
DO - 10.5220/0004851505430550