Application of HM-network with positive and negative claims for finding of memory volumes in information systems

• Autorzy: Matalytski Mikhail , Zając Paweł

Identyfikatory

DOI

10.17512/jamcm.2019.1.04

• Języki publikacji: EN

Abstrakty

EN

To solve the problem of determining the volume of memory of information systems (IS), it was suggested to use a stochastic model based on the use of HM (Howard-Matalytski) - a queueing network with revenues. This model allows you to take into account the dependencies of the processing times of messages (claims) on their volumes, the possibility of changing the volume of messages over time, and also the possibility of getting computer viruses into it. In such cases the processing of messages in nodes can be interrupted for some random time. Expressions are obtained for the mean (expected) values of the total message volumes in the IS nodes.

Słowa kluczowe

EN

HM-networks; information systems; volume of claim; positive claims; negative claims

PL

sieci HM; systemy informacyjne; roszczenie pozytywne; roszczenie negatywne

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2019
• Tom: Vol. 18, nr 1
• Strony: 41--51
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Matalytski Mikhail

• Institute of Mathematics, Czestochowa University of Technology Czestochowa, Poland

autor

Zając Paweł

• Institute of Mathematics, Czestochowa University of Technology Czestochowa, Poland

Bibliografia

• [1] Tikhonenko, O. (2006). Metody probalistycznne analizy systemów informacyjnych. Warszawa: Akademicka Oficyna Wydawnicza EXIT.
• [2] Matalytski, M., Tikhonenko, O., & Koluzaeva, E. (2011). Systems and queueing networks: analysis and application [In Russian: Sistemy i seti massovogo obsluzhivania: analiz i primenenija]. Grodno; GrSU.
• [3] Bufferbloat [Electronic resource]: Wikipedia: http: // en. wikipedia. org /wiki/
• [4] Matalytski, M., & Naumenko, V. (2016). Stochastic networks with nonstandard moving customers [In Russian: Stokhasticheskie seti s nestandartnym peremeshcheniem zaiavok]. Grodno; GrSU.
• [5] Matalytski, M., & Naumenko, V. (2015). Zastosowanie HM-sieci kolejkowych dla wyznaczenia objętości pamięci systemów informacyjnych. Studia Informatica, 35(3), 63-69.
• [6] Matalytski, M., & Zając, P. (2015). Finding of expected volumes of request in systems of the queueing network with limited amount of spaces of expectations. Studia Informatica, 38(1), 89-104.
• [7] Zając, P., & Matalytski, M. (2017). Expected volumes of requests in systems of the queueing network with a limited number of waiting places. Scientific Issues Jan Długosz University in Częstochowa Mathematics XXII, 141-160.
• [8] Matalytski, M., & Zajac, P. (2017). Application of HM-networks with impatient claims in finding the memory capacity in the information systems. Journal of Applied Mathematics and Computational Mechanics, 16(3), 47-59.
• [9] Matalytski, M., Zajac, P., & Kopats, D. (2018). Application of HM-networks with unreliable systems for finding the memory capacity in the information systems. Journal of Applied Mathematics and Computational Mechanics, 17(2).
• [10] Gelenbe, E. (1991). Product form queueing networks with negative and positive customers. Journal of Applied Probability, 28, 656-663.
• [11] Gelenbe, E., Glynn, P., & Sigman, K. (1991). Queues with negative customers. Journal of Applied Probability, 28, 242-250.
• [12] Gelenbe, E., & Schassberger, R. (1992). Stability of G-networks. Probability and its Applications in Engineering and the Information Sciences. Cambridge University Press. 6, 271-276.
• [13] Shanguin, V. (2013). Information security of computer systems and networks. M.: Vocational Education. Infra-M., 416.
• [14] Schneier, B. (2000). Secrets and Lies. Data Security in the Digital World. St. Petersburg.: Petersburg, 432.
• [15] Bocharov, P.P., & Vishnevsky, V.M. (2003). G-networks: development of the theory of multiplicative networks. Automation and Telemechanics, 5, 46-74.
• [16] Gelenbe, E. (1994). G-networks: a unifying model for neural and queueing networks. Annals of Operations Research, 48, 433-461.
• [17] Gelenbe, E. (1989). Random neural Networks with negative and positive signals and product form solution. Neural Computation, 1, 502-510.
• [18] Kim, H., & Gelenbe, E. (2013). G-networks towards synthetic biology. A brief review. Conf Proc. IEEE Eng. Med. Biol. Soc. 1, 579-583.
• [19] Artalejo, A., & Jesus, R. (2000). G-networks: A versatile approach for work removal in queueing networks. European Journal of Operational Research, 126, 233-249.
• [20] Artalejo, J.R. (1996). Retrial queues with negative arrivals. Proceedings of International Conference on Stochastic Processes. Cochin, 159-168.
• [21] Boucherie, R.J., & van Dijk, N.M. (1994). Local balance in queueing networks with positive and negative customers. Annals of Operations Research, 48, 463-492.
• [22] Chao, X., & Zheng, S. (1998). A result on networks of queues with customer coalescence and state-dependent signalling. Journal of Applied Probability, 35, 151-164.
• [23] Chao, X. (1995). Networks of queues with customers, signals and arbitrary service time distributions. Operations Research, 43, 537-550.
• [24] Fourneau, J.M., Gelenbe, E., & Surosc, R. (1996). G-networks with multiple classes of negative and positive customers. Theoretical Computer Science, 155, 1, 141-156.
• [25] Gelenbe, E. (2002). G-networks: Multiple classes of positive customers, signals, and product form. Results performance evaluation of complex systems: Techniques and Tools. Lecture Notes in Computer Science. 1-16.
• [26] Gelenbe, E. (1993). G-networks with instantaneous customer movement. Journal of Applied Probability, 30, 742-748.
• [27] Gelenbe, E., & Labed, A. (1998). G-networks with multiple classes of signals and positive customers. European Journal of Operational Research, 108, 293-305.
• [28] Gelenbe, E. & Fourneau, J.M. (2002). G-Networks with resets. Performance Evaluation, 49, 179-192.
• [29] Gantmakher, F.R. (1988). Theory of Matrices. M.: Science, 547.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).