Shaki, Yair Y. A Jackson network under general regime. (English) Zbl 1423.62167 Braz. J. Probab. Stat. 33, No. 3, 532-548 (2019). Summary: We consider a Jackson network in a general heavy traffic diffusion regime with the \(\alpha\)-parametrization. We also assume that each customer may abandon the system while waiting. We show that in this regime the queue-length process converges to a multi-dimensional regulated Ornstein-Uhlenbeck process. MSC: 62P30 Applications of statistics in engineering and industry; control charts 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) 90B20 Traffic problems in operations research 90B22 Queues and service in operations research 60J60 Diffusion processes Keywords:Jackson network; diffusion limits; many-server queue; heavy traffic; conventional diffusion regime; Halfin-Whitt regime; queue-length process; Ornstein-Uhlenbeck process × Cite Format Result Cite Review PDF Full Text: DOI Euclid References: [1] Atar, R. (2012). A diffusion regime with non-degenerate slowdown. Operations Research2, 490-500. · Zbl 1248.90038 [2] Atar, R. and Solomon, N. (2011). Asymptotically optimal interruptible service policies for scheduling jobs in a diffusion regime with nondegenerate slowdown. Queueing Systems3, 217-235. · Zbl 1244.90061 [3] Azaron, A. and Fatemi Ghomi, S. M. T. (2003). Optimal control of service rates and arrivals in Jackson networks. European Journal of Operational Research147, 17-31. · Zbl 1011.90016 [4] Billingsley, P. (1999). Convergence of Probability Measures. John Wiley and Sons, Inc. · Zbl 0944.60003 [5] Bremaud, P. (1981). Point Processes and Queues, Martingale Dynamics. New York: Springer. · Zbl 0478.60004 [6] Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S. and Zhao, L. (2005). Statistical analysis of a telephone call center. Journal of the American Statistical Association469, 36-50. · Zbl 1117.62303 [7] Chen, H. and Yao, D. D. (2001). Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization. New York: Springer. · Zbl 0992.60003 [8] Dupuis, P. and Ishii, H. (1991). On Lipschitz continuity of the solution mapping to the Skorokhod problem, with applications. Stochastics and Stochastics Reports1, 31-62. · Zbl 0721.60062 · doi:10.1080/17442509108833688 [9] Gamarnik, D. and Zeevi, A. (2006). Validity of heavy traffic steady-state approximations in generalized Jackson networks. The Annals of Applied Probability1, 56-90. · Zbl 1094.60052 [10] Halfin, S. and Whitt, W. (1981). Heavy-traffic limits for queues with many exponential servers. Operations Research3, 567-588. · Zbl 0455.60079 [11] Harrison, J. M. and Reiman, M. I. (1981). Reflected Brownian motion on an orthant. The Annals of Probability9, 302-308. · Zbl 0462.60073 [12] Hassin, R., Shaki, Y. Y. and Yovel, U. (2015). Optimal service capacity allocation in a loss system. Naval Research Logistics62, 81-97. · Zbl 1310.90025 [13] Kleinrock, R. L. (1964). Communication Nets: Stochastic Message Flow and Delay. New York: Dover Publications, Inc. · Zbl 0274.90012 [14] Mandelbaum, A. (2003). QED Q’s. Notes from a lecture delivered at the Workshop on Heavy Traffic Analysis and Process Limits of Stochastic Networks. EURANDOM. http://ie.technion.ac.il/serveng/References/references.html. [15] Mandelbaum, A., Massey, W. A. and Reiman, M. (1998). Strong approximations for Markovian service networks. Queueing Systems30, 149-201. · Zbl 0911.90167 [16] Protter, P. E. (2004). Stochastic Integration and Differential Equations, 2nd ed. Berlin: Springer. · Zbl 1041.60005 [17] Reed, J. E. and Ward, A. R. A. (2004). Diffusion approximation for a generalized Jackson network with reneging. In Proceedings of the 42nd Annual Conference on Communication, Control, and Computing, Sept. 29-Oct. 1. [18] Reiman, M. I. (1984). Open queueing networks in heavy traffic. Mathematics of Operations Research9, 441-458. · Zbl 0549.90043 [19] Wein, L. M. (1989). Capacity allocation in generalized Jackson networks. Operations Research Letters8, 143-146. · Zbl 0676.90024 [20] Whitt, W. (2003). How multiserver queues scale with growing congestion-dependent demand. Operations Research4, 531-542. · Zbl 1165.90412 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.