Computer network queuing models with intelligent call admission control mechanisms

• Autorzy: Oniszczuk W.
• Języki publikacji: EN

Abstrakty

EN

A new idea of intelligent call admission control mechanism embedded into a priority closed computer network is proposed. Here, the investigated closed network was reduced to two service centers. Such reduction allows for decomposition of a bigger network into a chain of individual queues, where each queue can be studied in isolation. At the beginning, a new algorithm to analyze two-node closed priority queuing network is presented. In this model, there exists one node containing several priority sources designated as an infinite server (IS), which generates task streams. There is also another node, a service center, which collects the arriving tasks and consumes them according to head-of-line (HOL) strategy. Such types of networks are described in the queuing theory as a finite-source, non-preemptive priority M/G/l/N queues. Later on, an idea of the intelligent admission control mechanism based on the Hidden Markov Models (HMMs) theory is described. This kind of mechanism is embedded into computer network with HOL scheduling discipline to regulate the input stream intensities.

Słowa kluczowe

EN

queueing theory; computer network; intelligent control; intelligent call admission control mechanisms; hidden Markov models theory

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2005
• Tom: Vol. 31, no 1
• Strony: 73--87
• Opis fizyczny: Bibliogr. 21 poz., wykr.

Twórcy

autor

Oniszczuk W.

• Faculty of Computer Science, Bialystok University of Technology, ul. Wiejska 45A, 15-351 Białystok, Poland,

Bibliografia

• [1] Baum L. E., An Inequality and Associated Maximization Technique in Statistical Estimation of Probabilistic Functions of Markov Process, Inequalities, Vol. 3, 1972, 1-8.
• [2] Baum L. E., Egon J. A., An Inequality with Applications to Statistical Estimation for Probabilistic Functions of Markov Process and to a Model for Ecology, Bulletin of the American Mathematical Society, Vol. 73, 1967, 360-363.
• [3] Baum L. E., Petrie T., Soûles G., Weiss N., A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, Annals of Mathematical Statistics, Vol. 41, No. 1, 1970, 164-171.
• [4] Bourlard H. a., Morgan N., Connectionist Speech Recognition, Kluwer Academic Publishers, Boston 1994.
• [5] Bryant R. M., Krzesiński A. E., Lakshmi M. S., Chandy K. M., The MVA Priority Approximation, ACM Transaction on Computer Systems, Vol. 2, No. 4, 1984, 335-359.
• [6] Czachórski T., Modele kolejkowe w ocenie efektywności sieci i systemów komputerowych. Pracownia Komputerowa Jacka Skalmierskiego, Gliwice 1999.
• [7] Cheng S.-T., Chen C.-M., Chen I.-R., Performance evaluation of an admission control algorithm: dynamic threshold with negotiation. Performance Evaluation, Vol. 52, 2003, 1-13.
• [8] Filipowicz B., Modelowanie i analiza sieci kolejkowych. Wydawnictwa AGH, Kralców 1997.
• [9] Grzech A., Sterowanie ruchem w sieciach teleinformatycznych. Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2002.
• [10] Jaiswal N. K., Priority Queues, Academic Press, New York and London 1968.
• [11] Laevens K., Bruneel H., Discrete-time multiserver queues with priorities. Performance Evaluation, Vol. 33, No. 4, 1998, 249-275.
• [12] Lavenberg S. S., Reiser M., Stationary state probabilities at arrival instants for closed queueing networks. Journal of Applied Probability, Vol. 17, No. 4, 1980, 1048-1061.
• [13] Morris R. J. T., Priority Queuing Networks, The Bell System Technical Journal, Vol. 60, No. 8, 1981, 1745-1769.
• [14] Oniszczuk W., An investigation of different classes of Hidden Markov Processes as signal models. Proceedings of the 1st International Conference on Digital Signal Processing and its Applications, DSPA-98, Moscow 1998, Vol. I, pp. r38-I'44.
• [15] Rabiner L. R., a Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, Vol. 77, No. 2, 1989, 257-286.
• [16] Rabiner L. R., Jaung B. H., Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, NJ 1993.
• [17] Reiser M., Mean-Value Analysis and Convolution Method for Queue-Dependent Servers in Closed Queueing Networks, Performance Evaluation, Vol. 1, 1981, 7-18.
• [18] Reiser M., Lavenberg S. S., Mean-Value Analysis of Closed Multichain Queuing Networks, Journal of the Association for Computing Machinery, Vol. 27, No. 2, 1980, 313-322.
• [19] Stavrakakis I., Chen G., Study of a scheduling policy for diverse deadline-based quality of service. Performance Evaluation, Vol. 41, 2000, 37-62.
• [20] Walraevens J., Steyaert B., Bruneel H., Delay characteristics in discrete-time Gl-G-1 queues with non-preemptive priority queueing discipline. Performance Evaluation, Vol. 50, 2002, 53-75.
• [21] Zgrzywa A., Ocena wydajności systemów informacyjnych metodami kolejkowymi. Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 1998.