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Time Domain Measurement Representation in Computer System Diagnostics and Performance Analysis

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Języki publikacji
EN
Abstrakty
EN
Time analysis is a common approach for testing and detecting methods for the performance analysis of computer systems. In the article it is shown, that measuring and identifying performances based on a benchmark is not sufficient for the proper analysis of the computer systems behavior. The response time of the process is often composed of the execution of many subprocesses or many paths of execution. Under this assumption, it is presented, that both convolution and deconvolution methods can be helpful in obtaining time distributions and modeling of complex processes. In such a modeling the analysis of measurement errors is very important and was taken into consideration. The example of using the methods in buffering process is also discussed.
Rocznik
Strony
53--64
Opis fizyczny
Bibliogr. 20 poz., rys., wykr.
Twórcy
autor
  • Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology
autor
  • Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology
autor
  • Faculty of Automatic Control, Electronic and Computer Science, Silesian University of Technology
Bibliografia
  • [1] G. Pratl, D. Dietrich, G. P. Hancke, and W. T. Penzhorn, “A new model for autonomous, networked control systems,” IEEE Transactions on Industrial Informatics, Vol. 3, No. 1, feb. 2007, pp. 21 –32.
  • [2] D. J. Lilja, Measuring Computer Performance: A Practitioner’s Guide. Cambridge University Press, 2005. [Online]. http: //books.google.pl/books?id=R8RLniX5DNQC
  • [3] E. D. Lazowska, Quantitative system performance: computer system analysis using queueing network models. Prentice-Hall, 1984. [Online]. http://books.google.pl/books?id=NNZQAAAAMAAJ
  • [4] D. A. Menascé, V. A. F. Almeida, L. W. Dowdy, and L. Dowdy, Performance by design: computer capacity planning by example. Prentice Hall PTR, New Jersey, USA, 2004.
  • [5] G. Bjedov, “Using open source tools for performance testing,” Google London Test Automation Conference (LTAC) Google Tech Talks, September 2006. [Online]. http://video.google.com
  • [6] R. Blum, Network performance open source toolkit: using Netperf, tcptrace, NIST Net, and SSFNet. Wiley Pub., 2003. [Online]. http: 64 Stanisław Wideł, Jarosław Flak, Piotr Gaj //books.google.pl/books?id=ECt5ycQ9D7YC
  • [7] T. Czachórski, Modele kolejkowe systemów komputerowych, ser. Skrypty Uczelniane - Politechnika Slaska. Wydawnictwo Politechniki Slaskiej, 1994. [Online]. http: //books.google.pl/books?id=50KkPgAACAAJ
  • [8] J. D. C. Little, “A proof for the queuing formula: L = W,” Operations Research, Vol. 9, No. 3, May/June 1961, pp. 383–387.
  • [9] S. Stidham, Jr., “A last word on L = W,” Operations Research, Vol. 22, No. 2, March/April 1974, pp. 417–421.
  • [10] F. Beutler, “Mean sojourn times in Markov queueing networks: Little’s formula revisited,” Information Theory, IEEE Transactions on, Vol. 29, No. 2, mar 1983, pp. 233 – 241.
  • [11] P. W. Glynn and W. Whitt, “Extensions of the queueing relations L = W and H = G,” Operations Research, Vol. 37, No. 4, July/August 1989, pp. 634–644.
  • [12] L. Lipsky, Queueing Theory A Linear Algebraic Approach. Springer New York, 2009. [Online]. http://dx.doi.org/10.1007/978-0-387-49706-8_3
  • [13] J. P. Lehoczky, “Real-time queueing theory,” in Real-Time Systems Symposium, 1996., 17th IEEE, dec 1996, pp. 186 –195.
  • [14] J. L. Diaz, D. F. Garcia, K. Kim, C.-G. Lee, L. Lo Bello, J. M. Lopez, S. L. Min, and O. Mirabella, “Stochastic analysis of periodic real-time systems,” in Real-Time Systems Symposium, 2002. RTSS 2002. 23rd IEEE, 2002, pp. 289 – 300.
  • [15] L. Abeni, N. Manica, and L. Palopoli, “Efficient and robust probabilistic guarantees for real-time tasks,” Journal of Systems and Software, Vol. 85, No. 5, 2012, pp. 1147 – 1156. [Online]. http://www.sciencedirect.com/science/article/pii/S0164121211003232
  • [16] M. Santos, B. Lisper, G. Lima, and V. Lima, “Sequential composition of execution time distributions by convolution,” Proc. 4th Workshop on Compositional Theory and Technology for Real-Time Embedded Systems (CRTS 2011), November 2011, pp. 30–37.
  • [17] J. Yeh, Real analysis: theory of measure and integration. World Scientific, Danvers, USA, 2006. [Online]. http://dx.doi.org/10.1007/978-0-387-49706-8_3
  • [18] A. Mattuck, Introduction to analysis. Prentice Hall, 1999. [Online]. http://books.google.pl/books?id=N0FkQgAACAAJ
  • [19] S. Wideł, J. Flak, and P. Gaj, “Interpretation of dual peak time signal measured in network systems,” in Computer Networks, ser. Communications in Computer and Information Science, A. Kwiecien, P. Gaj, and P. Stera, Eds. Springer Berlin Heidelberg, 2010, Vol. 79, pp. 141–152. [Online]. http://dx.doi.org/10.1007/978-3-642-13861-4_14
  • [20] ——, “Analysis of time measurements in network systems using decomposition on subprocesses,” in Computer Networks, ser. Communications in Computer and Information Science, A. Kwiecien, P. Gaj, and P. Stera, Eds. Springer Berlin Heidelberg, 2011, Vol. 160, pp. 70–79. [Online]. http://dx.doi.org/10.1007/978-3-642-21771-5_9
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5a9059ac-7b27-4e23-9c6c-e5d1a2c8bca5
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