RUS  ENG
Full version
JOURNALS // Avtomatika i Telemekhanika // Archive

Avtomat. i Telemekh., 2022 Issue 8, Pages 81–99 (Mi at16021)

This article is cited in 1 paper

Stochastic Systems

Asymptotic analysis of resource heterogeneous QS $(\text{MMPP}+2\text{M})^{(2,\nu)}/\text{GI}(2)/\infty$ under equivalently increasing service time

S. P. Moiseevaa, T. V. Bushkovaa, E. V. Pankratovab, M. P. Farkhadovb, A. A. Imomovc

a Tomsk State University, Tomsk, 634050 Russia
b Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, 117997 Russia
c Karshi State University, Karshi, 180119 Uzbekistan

Abstract: We consider a resource heterogeneous queuing system with a flexible two-node request-response facility. Each node has a certain resource capacity for service (buffer space) and hence a potential to respond to an incoming demand that generates a request for the provision of some random amount of resources for some random time. The request flows are steady-state Poisson flows of varying intensity. If it is required to use the resources of both nodes to serve a request, then it is assumed that the moments of arrival of such requests form an MMPP flow with a division into two different types of requests. A distinctive feature of the systems under consideration is that the resource is released in the same amount as requested. To construct a multidimensional Markov process, we use the method of introducing an additional variable and dynamic probabilities. The problem of analyzing the total capacity of customers of each type is solved provided that the request servicing intensity is much lower than the incoming flow intensity and assuming that the servers have unlimited resources.

Keywords: infinite-server heterogeneous queuing system, resource system, parallel queuing, Markov modulated Poisson flow, asymptotic analysis.

Presented by the member of Editorial Board: V. M. Vishnevsky

Received: 19.01.2022
Revised: 24.03.2022
Accepted: 28.04.2022

DOI: 10.31857/S0005231022080050


 English version:
Automation and Remote Control, 2022, 83:8, 1213–1227

Bibliographic databases:


© Steklov Math. Inst. of RAS, 2024