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Performance Modeling of Cloud Systems by an Infinite-Server Queue Operating in Rarely Changing Random Environment

Author

Listed:
  • Svetlana Moiseeva

    (Institute of Applied Mathematics and Computer Science, National Research Tomsk State University, 36 Lenina Ave., 634050 Tomsk, Russia
    These authors contributed equally to this work.)

  • Evgeny Polin

    (Institute of Applied Mathematics and Computer Science, National Research Tomsk State University, 36 Lenina Ave., 634050 Tomsk, Russia
    These authors contributed equally to this work.)

  • Alexander Moiseev

    (Institute of Applied Mathematics and Computer Science, National Research Tomsk State University, 36 Lenina Ave., 634050 Tomsk, Russia
    These authors contributed equally to this work.)

  • Janos Sztrik

    (Faculty of Informatics, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
    These authors contributed equally to this work.)

Abstract

This paper considers a heterogeneous queuing system with an unlimited number of servers, where the parameters are determined by a random environment. A distinctive feature is that the parameters of the exponential distribution of the request processing time do not change their values until the end of service. Thus, the devices in the system under consideration are heterogeneous. For the study, a method of asymptotic analysis is proposed under the condition of extremely rare changes in the states of the random environment. We consider the following problem. Cloud node accepts requests of one type that have a similar intensity of arrival and duration of processing. Sometimes an input scheduler switches to accept requests of another type with other intensity and duration of processing. We model the system as an infinite-server queue in a random environment, which influences the arrival intensity and service time of new requests. The random environment is modeled by a Markov chain with a finite number of states. Arrivals are modeled as a Poisson process with intensity dependent on the state of the random environment. Service times are exponentially distributed with rates also dependent on the state of the random environment at the time moment when the request arrived. When the environment changes its state, requests that are already in the system do not change their service times. So, we have requests of different types (serviced with different rates) present in the system at the same time. For the study, we consider a situation where changes of the random environment are made rarely. The method of asymptotic analysis is used for the study. The asymptotic condition of a rarely changing random environment (entries of the generator of the corresponding Markov chain tend to zero) is used. A multi-dimensional joint steady-state probability distribution of the number of requests of different types present in the system is obtained. Several numerical examples illustrate the comparisons of asymptotic results to simulations.

Suggested Citation

  • Svetlana Moiseeva & Evgeny Polin & Alexander Moiseev & Janos Sztrik, 2025. "Performance Modeling of Cloud Systems by an Infinite-Server Queue Operating in Rarely Changing Random Environment," Future Internet, MDPI, vol. 17(10), pages 1-14, October.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:10:p:462-:d:1766766
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    References listed on IDEAS

    as
    1. M. Eisen & M. Tainiter, 1963. "Stochastic Variations in Queuing Processes," Operations Research, INFORMS, vol. 11(6), pages 922-927, December.
    2. U. Yechiali & P. Naor, 1971. "Queuing Problems with Heterogeneous Arrivals and Service," Operations Research, INFORMS, vol. 19(3), pages 722-734, June.
    Full references (including those not matched with items on IDEAS)

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