Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T03:39:51.943Z Has data issue: false hasContentIssue false

Markov random field models of multicasting in tree networks

Published online by Cambridge University Press:  19 February 2016

Kavita Ramanan*
Affiliation:
Bell Laboratories, Lucent Technologies
Anirvan Sengupta*
Affiliation:
Bell Laboratories, Lucent Technologies
Ilze Ziedins*
Affiliation:
University of Auckland
Partha Mitra*
Affiliation:
Bell Laboratories, Lucent Technologies
*
Postal address: Bell Laboratories, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA.
Postal address: Bell Laboratories, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA.
∗∗∗ Postal address: Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand.
Postal address: Bell Laboratories, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA.

Abstract

In this paper, we analyse a model of a regular tree loss network that supports two types of calls: unicast calls that require unit capacity on a single link, and multicast calls that require unit capacity on every link emanating from a node. We study the behaviour of the distribution of calls in the core of a large network that has uniform unicast and multicast arrival rates. At sufficiently high multicast call arrival rates the network exhibits a ‘phase transition’, leading to unfairness due to spatial variation in the multicast blocking probabilities. We study the dependence of the phase transition on unicast arrival rates, the coordination number of the network, and the parity of the capacity of edges in the network. Numerical results suggest that the nature of phase transitions is qualitatively different when there are odd and even capacities on the links. These phenomena are seen to persist even with the introduction of nonuniform arrival rates and multihop multicast calls into the network. Finally, we also show the inadequacy of approximations such as the Erlang fixed-point approximations when multicasting is present.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2002 

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

Ballardie, A. J., Francis, P. F. and Crowcroft, J. (1993). Core based trees. In Proc. SIGCOMM '93, Annual Tech. Conf., Association for Computing Machinery, New York, pp. 8595.Google Scholar
Baxter, R. J. (1982). Exactly Solved Models in Statistical Mechanics. Academic Press, London.Google Scholar
Brightwell, G. R. and Winkler, P. (1999). Graph homomorphisms and phase transitions. J. Combinatorial Theory B 77, 415435.Google Scholar
Brightwell, G. R. and Winkler, P. (2000). Gibbs measures and dismantlable graphs. J. Combinatorial Theory B 78, 141169.Google Scholar
Burman, D. Y., Lehoczky, J. P. and Lim, Y. (1984). Insensitivity of blocking probabilities in a circuit-switched network. J. Appl. Prob. 21, 850859.Google Scholar
Chiu, D. M., Hurst, S., Kadansky, M. and Wesley, J. (1998). TRAM: A tree-based reliable multicast protocol. Tech. Rep. TR-98–66, Sun Microsystems.Google Scholar
Devaney, R. L. (1989). An Introduction to Chaotic Dynamical Systems, 2nd edn. Addison-Wesley, Redwood City, CA.Google Scholar
Georgii, H.-O. (1988). Gibbs Measures and Phase Transitions. Walter de Gruyter, Berlin.Google Scholar
Gibbens, R. J., Hunt, P. J. and Kelly, F. P. (1990). Bistability in communication networks. In Disorder in Physical Systems, eds Grimmett, G. R. and Welsh, D. J. A., Oxford University Press, pp. 113127.Google Scholar
Karvo, J., Virtamo, J., Aalto, S. and Martikainen, O. Blocking of dynamic multicast connections. Telecommun. Systems 16, 467481.Google Scholar
Kelbert, M. Y. and Suhov, Y. M. (1990). Statistical physics and network theory. Unpublished manuscript.Google Scholar
Kelly, F. P. (1985). Stochastic models of computer communication systems. J. R. Statist. Soc. B 47, 379395.Google Scholar
Kelly, F. P. (1991). Loss networks. Ann. App. Prob. 1, 319378.Google Scholar
Louth, G. M. (1990). Stochastic networks: complexity, dependence and routing. , University of Cambridge.Google Scholar
Martin, P. P. (1991). Potts Models and Related Problems in Statistical Mechanics (Ser. Adv. Statist. Mechanics 5). World Scientific, Teaneck, NJ.Google Scholar
Paul, S., Sabnani, K. K., Lin, J. C. and Bhattacharya, S. (1997). Reliable multicast transport protocol (RMTP). IEEE J. Sel. Commun. 15, 407421.CrossRefGoogle Scholar
Ross, K. W. (1995). Multiservice Loss Models for Broadband Telecommunications Networks. Springer, Berlin.Google Scholar
Spitzer, F. (1975). Markov random fields on an infinite tree. Ann. Prob. 3, 387398.Google Scholar
Van den Berg, J. and Steif, J. E. (1995). Percolation and the hard-core lattice gas model. Stoch. Process. Appl. 49, 179197.Google Scholar
Zachary, S. (1983). Countable state space Markov random fields and Markov chains on trees. Ann. Prob. 11, 894903.Google Scholar
Zachary, S. (1985). Bounded, attractive and repulsive Markov specifications on trees and on the one-dimensional lattice. Stoch. Proc. Appl. 20, 247256.Google Scholar
Zachary, S. and Ziedins, I. (1999). Loss networks and Markov random fields. J. Appl. Prob. 36, 403414.CrossRefGoogle Scholar
Zeidler, E. (1985). Nonlinear Functional Analysis and Its Applications I. Springer, Berlin.Google Scholar