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July  2015, 11(3): 829-848. doi: 10.3934/jimo.2015.11.829

Performance analysis of buffers with train arrivals and correlated output interruptions

1. 

SMACS Research Group, TELIN Department, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent

2. 

Supply Networks and Logistics Research Center, Department of Industrial Management, Ghent University, Technologiepark 903, B-9052 Zwijnaarde

Received  September 2013 Revised  May 2014 Published  October 2014

In this paper, we study a discrete-time buffer system with a time-correlated packet arrival process and one unreliable output line. In particular, packets arrive to the buffer in the form of variable-length packet trains at a fixed rate of exactly one packet per slot. The packet trains are assumed to have a geometric length, such that each packet has a fixed probability of being the last of its corresponding train. The output line is governed by a Markovian process, such that the probability that the line is available during a slot depends on the state of the underlying $J$-state Markov process during that slot.
    First, we provide a general analysis of the state of the buffer system based on a matrix generating functions approach. This also leads to an expression for the mean buffer content. Additionally, we take a closer look at the distributions of the packet delay and the train delay. In order to make matters more concrete, we next present a detailed and explicit analysis of the buffer system in case the output line is governed by a $2$-state Markov process. Some numerical examples help to visualise the influence of the various model parameters.
Citation: Bart Feyaerts, Stijn De Vuyst, Herwig Bruneel, Sabine Wittevrongel. Performance analysis of buffers with train arrivals and correlated output interruptions. Journal of Industrial & Management Optimization, 2015, 11 (3) : 829-848. doi: 10.3934/jimo.2015.11.829
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show all references

References:
[1]

Performance Evaluation, 51 (2003), 1-31. Google Scholar

[2]

Proceedings of IEEE INFOCOM '95 (Boston, 2-6 April 1995), (1995), 338-344. doi: 10.1109/INFCOM.1995.515893.  Google Scholar

[3]

Performance Evaluation, 16 (1992), 5-20. doi: 10.1016/0166-5316(92)90064-N.  Google Scholar

[4]

Performance Evaluation, 3 (1983), 165-175. doi: 10.1016/0166-5316(83)90001-9.  Google Scholar

[5]

Electronics Letters, 19 (1983), 735-737. doi: 10.1049/el:19830501.  Google Scholar

[6]

Computer Networks and ISDN Systems, 25 (1993), 1267-1277. doi: 10.1016/0169-7552(93)90018-Y.  Google Scholar

[7]

IEEE Transactions on Communications, 42 (1994), 255-259. doi: 10.1109/TCOMM.1994.577026.  Google Scholar

[8]

IEE Proceedings-Communications, 145 (1998), 331-336. doi: 10.1049/ip-com:19982288.  Google Scholar

[9]

Stochastic Models, 23 (2007), 195-209. doi: 10.1080/15326340701300746.  Google Scholar

[10]

Queueing Systems, 15 (1994), 325-345. doi: 10.1007/BF01189244.  Google Scholar

[11]

Performance Evaluation, 29 (1997), 85-104. doi: 10.1016/S0166-5316(96)00006-5.  Google Scholar

[12]

IEEE Transactions on Communications, 38 (1990), 447-455. doi: 10.1109/26.52655.  Google Scholar

[13]

BIT Numerical Mathematics, 11 (1971), 168-174.  Google Scholar

[14]

Applied Mathematics and Computation, 115 (2000), 43-62. doi: 10.1016/S0096-3003(99)00134-4.  Google Scholar

[15]

Computers and Operations Research, 39 (2012), 2905-2914. doi: 10.1016/j.cor.2011.11.023.  Google Scholar

[16]

Operations Research Letters, 30 (2002), 17-18. doi: 10.1016/S0167-6377(01)00112-2.  Google Scholar

[17]

Performance Evaluation, 55 (2004), 277-298. doi: 10.1016/j.peva.2003.08.004.  Google Scholar

[18]

Performance Evaluation, 21 (1994), 151-161. doi: 10.1016/0166-5316(94)90032-9.  Google Scholar

[19]

Advances in Applied Probability, 28 (1996), 114-165. doi: 10.2307/1427915.  Google Scholar

[20]

Electronics Letters, 44 (2008), 61-63. doi: 10.1049/el:20083020.  Google Scholar

[21]

Performance Evaluation, 67 (2010), 432-450. doi: 10.1016/j.peva.2009.12.007.  Google Scholar

[22]

IEEE Transactions on Communications, 46 (1998), 1575-1579. Google Scholar

[23]

Journal of Applied Probability, 41 (2004), 524-534. doi: 10.1239/jap/1082999083.  Google Scholar

[24]

Performance Evaluation, 63 (2006), 315-340. Google Scholar

[25]

Computers & Operations Research, 35 (2008), 3969-3988. doi: 10.1016/j.cor.2007.06.001.  Google Scholar

[26]

Telecommunication Systems, 41 (2009), 267-277. Google Scholar

[27]

European Journal of Operations Research, 85 (1995), 161-177. doi: 10.1016/0377-2217(93)E0148-Q.  Google Scholar

[28]

Queueing Systems, 27 (1997), 153-178. doi: 10.1023/A:1019162014745.  Google Scholar

[29]

Advances in Applied Probability, 22 (1990), 676-705. doi: 10.2307/1427464.  Google Scholar

[30]

Stochastic Models, 7 (1991), 1-46. doi: 10.1080/15326349108807174.  Google Scholar

[31]

Journal of the Operations Research Society of Japan, 46 (2003), 319-341.  Google Scholar

[32]

SIAM, 2000. doi: 10.1137/1.9780898719512.  Google Scholar

[33]

Cambridge University Press, Cambridge, UK, 1987. Google Scholar

[34]

IEEE Communications Letters, 8 (2004), 544-546. doi: 10.1109/LCOMM.2004.833827.  Google Scholar

[35]

New York: Marcel Dekker, 1989.  Google Scholar

[36]

Proceedings of IEEE INFOCOM '93 (San Francisco, 28 March - 1 April 1993), (1993), 401-410. doi: 10.1109/INFCOM.1993.253336.  Google Scholar

[37]

Stochastic Models, 23 (2007), 489-512. doi: 10.1080/15326340701471158.  Google Scholar

[38]

Performance Evaluation, 32 (1998), 35-56. doi: 10.1016/S0166-5316(97)00015-1.  Google Scholar

[39]

Electronics Letters, 34 (1998), 1719-1721. doi: 10.1049/el:19981248.  Google Scholar

[40]

16th International conference on analytical and stochastic modelling techniques and applications (ASMTA) Madrid , Spain, Lecture Notes in Computer Science, 5513 (2009), 189-203. doi: 10.1007/978-3-642-02205-0_14.  Google Scholar

[41]

International Journal of Electronics and Communications, 51 (1997), 178-186. Google Scholar

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