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Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Using fuzzy control to maximize profits in service level management - References
by Y. Diao, J. L. Hellerstein, and S. Parekh

Cited references and notes

  1. See, for example, http://www.ietf.org/rfc/rfc2474.txt and http://www.ietf.org/rfc/rfc2208.txt.
  2. “What is a service level agreement (SLA),” Paradyne Corporation, available at http://www.paradyne.com/framesaver-minisite/why-frame-relay.html.
  3. Service Level Agreement, Telecommunications Infrastructure Fund Board, State of Texas, http://www.tifb.state.tx.us/.
  4. Local Area Network Infrastructure Maintenance, Service Level Agreement, University of Michigan Information Technology, http://www.itcom.itd.umich.edu/mainten/lan_standard_sla.html.
  5. Y. Diao, J. L. Hellerstein, and S. Parekh, “A Business-Oriented Approach to the Design of Feedback Loops for Performance Management,” Proceedings of the 12th International Workshop on Distributed Systems: Operations and Management, Nancy, France, October 2001, IEEE, New York (2001), pp. 11–22.
  6. K. Ogata, Modern Control Engineering, 3rd Edition, Prentice Hall, Englewood Cliffs, NJ (1997).
  7. D. Menasce, V. Almeida, R. Fonsece, and M. Mendes, “Business Oriented Resource Management Policies for E-commerce Servers,” Performance Evaluation 42, 223–239 (2000).
  8. D. Menasce, D. Barbara, and R. Dodge, “Preserving QoS of E-commerce Sites Through Self-Tuning: A Performance Model Approach,” Proceedings of 2001 ACM Conference on E-commerce, Tampa, FL, October 2001, ACM, New York (2001), pp. 224–234.
  9. Z. Liu, M. S. Squillante, and J. L. Wolf, “On Maximizing Service-Level-Agreement Profits,” Proceedings of the ACM Conference on Electronic Commerce (EC'01), ACM, New York (2001), pp. 213–223.
  10. S. Parekh, N. Gandhi, J. L. Hellerstein, D. M. Tilbury, and J. P. Bigus, “Using Control Theory to Achieve Service Level Objectives in Performance Management,” Proceedings of IEEE/IFIP Symposium on Integrated Network Management, IEEE, New York (2001), pp. 841–854.
  11. V. Misra, B. G. Wei, and D. Towsley, “Fluid Based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proceedings of the ACM SIGCOMM, ACM, New York (2000), pp. 151–160.
  12. C. Lu, J. A. Stankovic, T. F. Abdelzaher, G. Tao, S. Son, and M. Marley, “Performance Specifications and Metrics for Adaptive Real Time Systems,” Proceedings of the 21st IEEE Real Time Systems Symposium, IEEE, New York (2000), pp. 13–24.
  13. P. Fishburn and A. Odlyzko, “Dynamic Behavior of Differential Pricing and Quality of Service Options for the Internet,” Proceedings of the ACM First International Conference on Information and Computation Economics, ACM, New York (1998), pp. 128–139.
  14. B. Huberman and R. Lukose, “A Methodology for Managing Electronic Transactions over the Internet,” Proceedings of the Third International Conference on Computing in Economics and Finance, Stanford, CA, June 1997, Society for Computational Economics (1997), pp. 1–14.
  15. K. M. Passino and S. Yurkovich, Fuzzy Control, Addison-Wesley Longman, Menlo Park, CA (1998).
  16. K.-T. Sun, “Two-Dimensional Fuzzy Ranking Approach to Job Scheduling Problems,” Proceedings of IEEE International Conference on Fuzzy Systems, Piscataway, NJ, 1998, IEEE, New York (1998), pp. 873–878.
  17. M. Razaz, J. King, A. Rogers, and A. Duke, “Applications of Fuzzy Logic to Multimedia Technology,” Proceedings of the Second International ICSC Symposium on Fuzzy Logic and Applications (ISFL'97), ICSC Academic Press, Canada (1997), pp. 333–338.
  18. Y. C. Chen, “Dealing with Fuzziness in Bank Credit Granting Analysis,” Proceedings of the 8th International Fuzzy Systems Association World Congress, Taipei, Taiwan, August 1999, International Fuzzy Systems Association (1999), pp. 1092–1095.
  19. K. Weber and Z. Sun, “Fuzzy Stochastic Dynamic Programming for Marketing Decision Support,” International Journal of Intelligent Systems 15, No. 8, 763–783 (2000).
  20. R. Zhang and Y. A. Phillis, “Fuzzy Control of Arrivals to Tandem Queue with Two Stations,” IEEE Transactions on Fuzzy Systems 7, No. 3, 361–367 (1999).
  21. Autonomic Computing: IBM's Perspective on the State of Information Technology, IBM Research Division, http://www.research.ibm.com/autonomic/manifesto/autonomic_computing.pdf.
  22. Y. Diao, J. L. Hellerstein, and S. Parekh, “Stochastic Modeling of Lotus Notes with a Queueing Model,” Proceedings of the CMG 2001 International Conference, Anaheim, CA, December 2001, Computer Measurement Group (2001).
  23. G. F. Franklin, J. D. Powell, and A. Emani-Naeini, Feedback Control of Dynamic Systems, Third Edition, Addison-Wesley Publishing Co., Reading, MA (1994).
  24. There are alternative ways to justify this assertion. One example is to employ the notion of “holding per customer per time unit in system” as in Reference 20, which means that costs increase as response times rise even if throughput is unchanged.
  25. For simplicity; other functions such as trapezoid or Gaussian may also be used.
  26. H. K. Khalil, Nonlinear Systems, MacMillan, New York (1996).
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