Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-24T00:24:11.908Z Has data issue: false hasContentIssue false

ANALYSIS OF MULTI-RESOURCE LOSS SYSTEM WITH STATE-DEPENDENT ARRIVAL AND SERVICE RATES

Published online by Cambridge University Press:  19 April 2017

Valeriy Naumov
Affiliation:
Service Innovation Research Institute, Annankatu 8 A, 00120 Helsinki, Finland E-mail: valeriy.naumov@pfu.fi
Konstantin Samouylov
Affiliation:
Peoples' Friendship University of Russia (RUDN University), Miklukho-Maklaya St. 6, 117198 Moscow, Russian Federation E-mail: ksam@sci.pfu.edu.ru

Abstract

In this paper, we study a generalization of the classical multi-dimensional Erlang loss model with state-dependent arrival and service rates, in which customers at arrival occupy random quantities of various resources and release them at departure. Total amount of resources allocated to customers cannot exceed predefined maximum levels. There can be two types of customers: positive customers, which occupy positive quantities of resources, and negative customers, which occupy negative quantities of resources. Negative customers increase the amount of resources available to positive customers and therefore decrease blocking of positive customers caused by lack of resources.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Artalejo, J.R. (2000). G-networks: a versatile approach for work removal in queueing networks. European Journal of Operational Research 126(2): 233249.Google Scholar
2. Breuer, L. (2003). From Markov jump processes to spatial queues. New York: Springer Science & Business Media.Google Scholar
3. Fourneau, J.M. & Gelenbe, E. (2004). Flow equivalence and stochastic equivalence in G-networks. Computational Management Science 1(2): 179192.Google Scholar
4. Fourneau, J.M., Gelenbe, E., & Suros, R. (1996). G-networks with multiple classes of negative and positive customers. Theoretical Computer Science 155(1): 141156.Google Scholar
5. Gelenbe, E. (1989). Random neural networks with negative and positive signals and product form solution. Neural Computation 1(3): 502510.CrossRefGoogle Scholar
6. Gelenbe, E. (1991). Product-form queuing-networks with negative and positive customers. Journal of Applied Probability 28(2): 656663.Google Scholar
7. Gelenbe, E. (1993). G-Networks with signals and batch removal. Probability in the Engineering and Informational Sciences 7: 335342.Google Scholar
8. Gelenbe, E. & Fourneau, J.M. (2002). G-networks with resets. Performance Evaluation 49(1): 179191.Google Scholar
9. Naumov, V. & Samuoylov, K. (2016). On relationship between queuing systems with resources and Erlang networks. Informatics and Applications 10(2): 914.Google Scholar
10. Romm, E.L. & Skitovitch, V.V. (1971). On certain generalization of problem of Erlang. Automation and Remote Control 32(5): 10001003.Google Scholar
11. Tikhonenko, O.M. (1997). Determination of characteristics of queuing systems with limited memory. Automation and Remote Control 58(5): 969973.Google Scholar
12. Tikhonenko, O.M. (2005). Generalized Erlang problem for queueing systems with bounded total size. Problems of Information Transmission 41(2): 243253.Google Scholar
13. Tikhonenko, O.M. & Klimovich, K.G. (2001). Analysis of queuing systems for random-length arrivals with limited cumulative volume. Problems of Information Transmission 37(1): 7079.Google Scholar
14. Do, V.T. (2011). Bibliography on G-networks, negative customers and applications. Mathematical and Computer Modelling 53(1–2): 205212.CrossRefGoogle Scholar