Some preliminary results of memory cache analysis with the use of non-extensive

Dominik Strzałka


The problem of modeling different parts of computer systems requires accurate statistical tools. Cache memory systems is an inherent part of nowadays computer systems, where the memory hierarchical structure plays a key point role in behavior and performance of the whole system. In the case of Windows operating systems, cache memory is a place in memory subsystem where the I/O system puts recently used data from disk. In paper some preliminary results about statistical behavior of one selected system counter behavior are presented. Obtained results shown that the real phenomena, which have appeared during human-computer interaction, can be expressed in terms of non-extensive statistics that is related to Tsallis proposal of new entropy definition.


complex systems; cache memory; system counters;Tsallis entropy

Full Text:



Aristotle, Metaphysics (from The Complete Works of Aristotle: The Revised Oxford Translation), ed. J. Barnes, Princeton, 1984.

P. Wegner, Research paradigms in computer science, Proc. of the 2nd Int. Conf. on Soft. Eng., San Francisco, California, pp. 322-330, 1976.

M. M. Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos, Simon and Schuster, USA, 1992.

F. Grabowski, Nonextensive model of self-organizing systems, Complexity, vol. 18, no. 5, pp. 28-36, 2013.

C. Tsallis, Possible generalization of Boltzmann-Gibbs statistics, J. Stat. Phys., vol. 52, pp. 479–487, 1988.

C. Tsallis, F. Baldovin, R. Cerbino, P. Pierobon, Introduction to Nonextensive Statistical Mechanics and Thermodynamics, in The Physics of Complex Systems (New Advances and Perspectives), Eds. F. Mallamace, H.E. Stanley, IOS Press, 2004.

C. Tsallis, Nonextensive statistical mechanics, anomalous diffusion and central limit theorems, Milan J. Math., Vol. 73, pp. 145–176, 2005.

D. O. Cajueiro, B. M. Tabak, “Is the expression H = 1/(3-q) valid for real financial data?”, Physica A, vol. 373, pp. 593-602, 2007

A. Weron, K. Burnecki, S. Mercik and K. Weron, Complete description of all self-similar models driven by Lévy stable noise. Phys. Rev. E, 71 p. 016113, 2005.

P. Dymora, M. Mazurek, Network Anomaly Detection Based on the Statistical Self-similarity Factor, Lecture Notes in Electrical Engineering vol. 324(1), pp. 271-287, 2015.

Data publikacji: 2017-12-22 09:38:09
Data złożenia artykułu: 2017-12-22 09:34:11


Total abstract view - 501
Downloads (from 2020-06-17) - PDF - 0



  • There are currently no refbacks.

Copyright (c) 2017 Dominik Strzałka

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.