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Issue:An intuitionistic fuzzy approach for IT service-level-management

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http://ifigenia.org/wiki/issue:nifs/21/2/87-98
Title of paper: An intuitionistic fuzzy approach for IT service-level-management
Author(s):
Roland Schuetze
University of Fribourg, Switzerland
roland.schuetze@unifr.ch
Published in: "Notes on IFS", Volume 21, 2015, Number 2, pages 87—98
Download:  PDF (898  Kb, File info)
Abstract: Managing the quality of virtualized, distributed and multi-tiered services is a hot topic in today’s service research. IT-centric service levels, written in IT technical terms need to be bridged to business-oriented service achievements. Due to the financial impact of Service Level Agreements (SLAs) there is great research interest in integrated management tools that automatically monitor the performance of multi-tier applications, autonomously warn for arising problems and predict in case of incidents on possible frontend impacts like end-user experience or other business implications. These problems are known as root cause analysis and business impact analysis, respectively. In addition the impact of service levels defined for these technical services on customers' business processes, is difficult to estimate. Thus, it is a major objective to identify SLA’s that directly affect the performance of customers’ business departments. The proposed concept is providing a bridge between business impacts to distributed systems and technical components by defining dependency couplings in a practical and feasible manner in order to satisfy aspects of the distributed and fuzzy nature of SLA dependencies.
Keywords: Service level, SLA, Business impact, Services quality, Intuitionistic fuzzy sets.
AMS Classification: 03E72, 03E75.
References:
  1. Joshi, K. P., Joshi, A., & Yesha, Y. (2011) Managing the Quality of Virtualized Services, Proceedings of the SRII Service Research Conference.
  2. Hui Li (2009) Challenges in SLA Translation – SLA@SOI European Commission Seventh Framework Programme (2007-2013) SAP Research, Dec. 2009
  3. Schütze, R. (2013) Intuitionistic Component Failure Impact Analysis, Notes on Intuitionistic Fuzzy Sets, 19(3), 62–72.
  4. Dhama, H. (1995) Quantitative models, Journal of Systems and Software, 29, 65–74.
  5. Fenton, N., & Melton, A. (1990). Deriving software measures. Journal of Systems and Software, 12(3), 177–187.
  6. Alghamdi, J. S. (2007). Measuring software coupling. Proceedings of the 6th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems.
  7. Quynh, P. T. (2009) Dynamic Coupling Metrics for Service–Oriented Software, International Journal of Electrical & Electronics Engineering, 3(5), p. 282.
  8. Yang, H. Y. (2010) Measuring Indirect Coupling, Doctor Thesis at University of Auckland, New Zealand.
  9. Atanassov, K. (1999) Intuitionistic Fuzzy Sets: (Studies in Fuzziness and Soft Computing), Springer, Heidelberg.
  10. Zadeh, L. (1994) Soft Computing and Fuzzy Logic, IEEE Software, 11(6), 48–56.
  11. Kolev, B., & Ivanov, I. (2009) Fault Tree Analysis in an Intuitionistic Fuzzy Configuration Management Database, Notes on Intuitionistic Fuzzy Sets, 15(2), 10–17.
  12. Kosko, B. (1986) Fuzzy cognitive maps, International Journal of Man-Machine Studies, 24, 65–75.
  13. Stylios, C. D., Georgopoulos, V. C., & Groumpos, P. P. (1997) The use of Fuzzy Cognitive Maps in Modeling Systems, Proc. of 5th IEEE Mediterranean Conf. on Control and Systems, 518–527.
  14. Neo4j, World’s Leading Graph Database. http://neo4j.com/product/
  15. Robak, S., & Pieczynski, A. (2008) Fuzzy Modeling of QoS for e-Business Transactions Realized by e-Services, Journal of Applied Computer Science, 16(1), 69–79.
  16. Sora, I., Todinca, D., & Avram, C. (2009) Translating user preferences into fuzzy rules for the automatic selection of services, Proc. of 5th Int. Symp. Applied Computational Intelligence and Informatics (SACI'09), 497–502.
  17. Kieninger, A., Berghoff, F., Fromm, H., & Satzger, G. (2013) Simulation-based Quantification of Business Impacts Caused by Service Incidents, Proc. of the 4th Int. Conf. on Exploring Service Science. (Portugal) 2013 LNBIP, No.143. Springer. 170–185.
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