Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-28T07:56:30.928Z Has data issue: false hasContentIssue false

WAIT-AND-SEE STRATEGIES IN POLLING MODELS

Published online by Cambridge University Press:  25 November 2011

Frank Aurzada
Affiliation:
Technische Universität Berlin, Institut für Mathematik, 10623 Berlin, Germany. E-mail: aurzada@math.tu-berlin.de; sergej.beck@hotmail.de; ms@math.tu-berlin.de
Sergej Beck
Affiliation:
Technische Universität Berlin, Institut für Mathematik, 10623 Berlin, Germany. E-mail: aurzada@math.tu-berlin.de; sergej.beck@hotmail.de; ms@math.tu-berlin.de
Michael Scheutzow
Affiliation:
Technische Universität Berlin, Institut für Mathematik, 10623 Berlin, Germany. E-mail: aurzada@math.tu-berlin.de; sergej.beck@hotmail.de; ms@math.tu-berlin.de

Abstract

We consider a general polling model with N stations. The stations are served exhaustively and in cyclic order. Once a station queue falls empty, the server does not immediately switch to the next station. Rather, it waits at the station for the possible arrival of new work (“wait-and-see”) and, in the case of this happening, it restarts service in an exhaustive fashion. The total time the server waits idly is set to be a fixed, deterministic parameter for each station. Switchover times and service times are allowed to follow some general distribution, respectively. In some cases, which can be characterized, this strategy yields a strictly lower average queuing delay than for the exhaustive strategy, which corresponds to setting the “wait-and-see credit” equal to zero for all stations. This extends the results of Peköz [12] and of Boxma et al. [4]. Furthermore, we give a lower bound for the delay for all strategies that allow the server to wait at the stations even though no work is present.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2011

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.Boxma, O.J. & Groenendijk, W.P. (1987). Pseudo-conservation laws in cyclic-service systems. Journal of Applied Probability 24(4): 949964.CrossRefGoogle Scholar
2.Boxma, O.J., Levy, H. & Weststrate, J.A. (1991). Efficient visit frequencies for polling tables: minimization of waiting costs. Queueing Systems 9: 133162.CrossRefGoogle Scholar
3.Suhov, Yu. M. (ed.), Analytic methods in applied probability. Providence, RI: American Mathematical Society (AMS). Transl., Ser. 2, Am. Math. soc. 207.Google Scholar
4.Boxma, O.J., Schlegel, S. & Yechiali, U. (2002). Two-queue polling models with a patient server. Annals of Operations Research 112: 101121.CrossRefGoogle Scholar
5.Cooper, R.B., Niu, S. & Srinivasan, M.M. (1998). When does forced idle time improve performance in polling models? Management Science 44: 10791086.CrossRefGoogle Scholar
6.Cooper, R.B., Niu, S.-C. & Srinivasan, M.M. (1999). Setups in polling models: Does it make sense to set up if no work is waiting? Journal of Applied Probability 36(2): 585592.CrossRefGoogle Scholar
7.Kleinrock, L. (1975). Queueing systems. Volume I. New York: Wiley.Google Scholar
8.Kramer, G. (2005). Ethernet passive optical networks. New York: McGraw-Hill Communications Engineering.Google Scholar
9.Lam, C.F. (2007). Passive optical networks: Principles and practice. Amsterdam, Elsevier.Google Scholar
10.Liu, Z., Nain, P. & Towsley, D. (1992). On optimal polling policies. Queueing Systems, 11: 5983.CrossRefGoogle Scholar
11.Olsen, T.L. & Van der Mei, R.D. (2003). Polling systems with periodic server routing in heavy traffic: Distribution of the delay. Journal of Applied Probability 40(2): 305326.CrossRefGoogle Scholar
12.Peköz, E.A. (1999). More on using forced idle time to improve performance in polling models. Probability in the Engineering and Informational Sciences 13(4): 489496.CrossRefGoogle Scholar
13.Takagi, H. (1986). Analysis of polling models. Cambridge, MA: MIT Press.Google Scholar
14.Takagi, H. (1988). Queuing analysis of polling models. ACM Computing Surveys 20(1): 528.CrossRefGoogle Scholar
15.Dshalalow, Jewgeni H. (ed.), Frontiers in queueing: models and applications in science and engineering. Boca Raton, FL: CRC Press. Probability and Stochastics Series.Google Scholar
16.Yechiali, U. (2004). On the M X/G/1 queue with a waiting server and vacations. Sankhyā 66(1): 159174.Google Scholar