Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-13T05:06:07.017Z Has data issue: false hasContentIssue false

Overflow behavior in queues with many long-tailed inputs

Published online by Cambridge University Press:  01 July 2016

Michel Mandjes*
Affiliation:
Bell Laboratories, Lucent Technologies
Sem Borst*
Affiliation:
CWI
*
Postal address: Bell Laboratories, 600 Mountain Avenue, P.O. Box 636, Murray Hill, NJ 07974, USA.
∗∗ Postal address: CWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands. Email address: sem@cwi.nl

Abstract

We consider a fluid queue fed by the superposition of n homogeneous on-off sources with generally distributed on and off periods. The buffer space B and link rate C are scaled by n, so that we get nb and nc, respectively. Then we let n grow large. In this regime, the overflow probability decays exponentially in the number of sources n. We specifically examine the scenario where b is also large. We obtain explicit asymptotics for the case where the on periods have a subexponential distribution, e.g., Pareto, Lognormal, or Weibull.

The results show a sharp dichotomy in the qualitative behavior, depending on the shape of the function v(t) := - logP(A* > t) for large t, A* representing the residual on period. If v(.) is regularly varying of index 0 (e.g., Pareto, Lognormal), then, during the path to overflow, the input rate will only slightly exceed the link rate. Consequently, the buffer will fill ‘slowly’, and the typical time to overflow will be ‘more than linear’ in the buffer size. In contrast, if v(.) is regularly varying of index strictly between 0 and 1 (e.g., Weibull), then the input rate will significantly exceed the link rate, and the time to overflow is roughly proportional to the buffer size.

In both cases there is a substantial fraction of the sources that remain in the on state during the entire path to overflow, while the others contribute at their mean rates. These observations lead to approximations for the overflow probability. The approximations may be extended to the case of heterogeneous sources. The results provide further insight into the so-called reduced-load approximation.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2000 

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] Agrawal, R., Makowski, A. M. and Nain, Ph. (1999). On a reduced load equivalence for fluid queues under subexponentiality. Queueing Systems 33, 541.CrossRefGoogle Scholar
[2] Anantharam, V. (1988). How large delays build up in a GI/G/1 queue. Queueing Systems 5, 345368.Google Scholar
[3] Anick, D., Mitra, D. and Sondhi, M. M. (1982). Stochastic theory of a data-handling system with multiple sources. Bell System Tech. J. 61, 18711894.CrossRefGoogle Scholar
[4] Asmussen, S. (1987). Applied Probability and Queues. John Wiley, New York.Google Scholar
[5] Asmussen, S., Klüppelberg, C. and Sigman, K. (1999). Sampling at subexponential times, with queueing applications. Stoch. Proc. Appl. 79, 265286.CrossRefGoogle Scholar
[6] Beran, J., Sherman, R., Taqqu, M. S. and Willinger, W. (1995). Long-range dependence in variable-bit-rate video traffic. IEEE Trans. Commun. 43, 15661579.CrossRefGoogle Scholar
[7] Bingham, N. H., Goldie, C. and Teugels, J. (1987). Regular Variation (Encyclopedia Math. Appl. 27). Cambridge University Press.Google Scholar
[8] Botvich, D. D. and Duffield, N. G. (1995). Large deviations, the shape of the loss curve, and economies of scale in large multiplexers. Queueing Systems 20, 293320.CrossRefGoogle Scholar
[9] Boxma, O. J. (1996). Fluid queues and regular variation. Perf. Eval. 27 & 28, 699712.Google Scholar
[10] Boxma, O. J. (1997). Regular variation in a multi-source fluid queue. In Teletraffic Contributions for the Information Age (Proc. 15th Int. Teletraffic Congress), eds Ramaswami, V. and Wirth, P. E.. Elsevier, Amsterdam, pp. 391402.Google Scholar
[11] Boxma, O. J. and Dumas, V. (1998). Fluid queues with long-tailed activity period distributions. Comput. Commun. 21, 15091529.CrossRefGoogle Scholar
[12] Cohen, J. W. (1973). Some results on regular variation for distributions in queueing and fluctuation theory. J. Appl. Prob. 10, 343353.CrossRefGoogle Scholar
[13] Courcoubetis, C. and Weber, R. R. (1996). Buffer overflow asymptotics for a buffer handling many traffic sources. J. Appl. Prob. 33, 886903.Google Scholar
[14] Duffield, N. G. (1996). Economies of scale in queues with sources having power-law large deviation scalings. J. Appl. Prob. 33, 840857.CrossRefGoogle Scholar
[15] Duffield, N. G. (1998). Queueing at large resources driven by long-tailed M/G/∞-modulated processes. Queueing Systems 28, 245266.Google Scholar
[16] Duffield, N. G. and O'Connell, N. (1995). Large deviations and overflow probabilities for the general single-server queue, with applications. Proc. Cambr. Philos. Soc. 118, 363374.CrossRefGoogle Scholar
[17] Dumas, V. and Simonian, A. (2000). Asymptotic bounds for the fluid queue fed by subexponential on/off sources. Adv. Appl. Prob. 32, 244255.Google Scholar
[18] Elwalid, A. I. and Mitra, D. (1991). Analysis and design of rate-based congestion control of high speed networks, I: stochastic fluid models, access regulation. Queueing Systems 9, 2964.CrossRefGoogle Scholar
[19] Erramilli, A., Singh, R. P. and Pruthi, P. (1994). Chaotic maps as model of packet traffic. In The Fundamental Role of Teletraffic in the Evaluation of Telecommunications Networks (Proc. 14th Int. Teletraffic Congress), eds Labetoulle, J. and Roberts, J. W.. Elsevier, Amsterdam, pp. 329338.CrossRefGoogle Scholar
[20] Grossglauser, M. and Bolot, J.-C. (1999). On the relevance of long-range dependence in network traffic. IEEE/ACM Trans. Networking 7, 629640.Google Scholar
[21] Heath, D., Resnick, S. and Samorodnitsky, G. (1997). How system performance is affected by the interplay of averages in a fluid queue with long range dependence induced by heavy tails. Ann. Appl. Prob. 9, 352375.Google Scholar
[22] Heyman, D. and Lakshman, T. V. (1996). What are the implications of long-range dependence for VBR traffic engineering? IEEE/ACM Trans. Networking 4, 301317.Google Scholar
[23] Jelenković, P. R. and Lazar, A. A. (1999). Asymptotic results for multiplexing subexponential on–off processes. Adv. Appl. Prob. 31, 394421.Google Scholar
[24] Kella, O. and Whitt, W. (1992). A storage model with a two-state random environment. Operat. Res. 40(S2), S257S262.Google Scholar
[25] Kosten, L. (1974). Stochastic theory of a multi-entry buffer, part 1. Delft Prog. Rept, Ser. F 1, 1018.Google Scholar
[26] Kosten, L. (1984). Stochastic theory of data-handling systems with groups of multiple sources. In Performance of Computer-Communication Systems, eds Rudin, H. and Bux, W.. Elsevier, Amsterdam, pp. 321331.Google Scholar
[27] Leland, W. E., Taqqu, M. S., Willinger, W. and Wilson, D. V. (1994). On the self-similar nature of Ethernet traffic. IEEE/ACM Trans. Networking 2, 115.Google Scholar
[28] Likhanov, N. and Mazumdar, R. R. (1998). Cell loss asymptotics in buffers fed with a large number of independent stationary sources. In Proc. IEEE Infocom '98. IEEE Computer Society Press, Silver Spring, MD, pp. 339346.Google Scholar
[29] Likhanov, N. and Mazumdar, R. R. (2000). Cell loss asymptotics in buffers fed by heterogeneous long-tailed sources. In Proc. IEEE Infocom 2000. IEEE Computer Society Press, Silver Spring, MD, pp. 173180.Google Scholar
[30] Likhanov, N., Tsybakov, B. and Georganas, N. D. (1995). Analysis of an ATM buffer with self-similar (‘fractal’) input traffic. In Proc. IEEE Infocom '95. IEEE Computer Society Press, Silver Spring, MD, pp. 985992.Google Scholar
[31] Liu, Z., Nain, Ph., Towsley, D. and Zhang, Z.-L. (1999). Asymptotic behavior of a multiplexer fed by a long-range dependent process. J. Appl. Prob. 36, 105118.CrossRefGoogle Scholar
[32] Mandjes, M. and Kim, J.-H. (2001). Large deviations for small buffers: an insensitivity result. To appear in Queueing Systems.CrossRefGoogle Scholar
[33] Mandjes, M. and Ridder, A. (1999). Optimal trajectory to overflow in a queue fed by a large number of sources. Queueing Systems 31, 137170.Google Scholar
[34] Norros, I. (1994). A storage model with self-similar input. Queueing Systems 16, 387396.Google Scholar
[35] Norros, I. (1995). On the use of fractional Brownian motion in the theory of connectionless networks. IEEE J. Sel. Areas Commun. 13, 953962.Google Scholar
[36] Pakes, A. G. (1975). On the tail of waiting time distributions. J. Appl. Prob. 12, 555564.Google Scholar
[37] Parulekar, M. and Makowski, A. M. (1997). Tail probabilities for a multiplexer driven by M/G/∞ input processes (I): preliminary asymptotics. Queueing Systems 27, 271296.Google Scholar
[38] Paxson, V. and Floyd, S. (1995). Wide area traffic: the failure of Poisson modeling. IEEE/ACM Trans. Networking 3, 226244.Google Scholar
[39] Resnick, S. and Samorodnitsky, G. (1999). Activity periods of an infinite server queue and performance of certain heavy tailed fluid queues. Queueing Systems 33, 4371.Google Scholar
[40] Resnick, S. and Samorodnitsky, G. (1999). Steady state distribution of the buffer content for M/G/∞ input fluid queues. Tech. Rept TR1242, Cornell University.Google Scholar
[41] Rockafellar, R. T. (1970). Convex Analysis. Princeton University Press.CrossRefGoogle Scholar
[42] Ryu, B. K. and Elwalid, A. I. (1996). The importance of long-range dependence of VBR video traffic in ATM traffic engineering: myths and realities. Comput. Commun. Rev. 26, 314.Google Scholar
[43] Simonian, A. and Guibert, J. (1995). Large deviations approximation for fluid queues fed by a large number of on/off sources. IEEE J. Sel. Areas Commun. 13, 10171027.Google Scholar
[44] Stern, T. E. and Elwalid, A. I. (1991). Analysis of separable Markov-modulated rate models for information-handling systems. Adv. Appl. Prob. 23, 105139.Google Scholar
[45] Weiss, A. (1986). A new technique of analyzing large traffic systems. Adv. Appl. Prob. 18, 506532.Google Scholar
[46] Willinger, W., Taqqu, M. S., Sherman, R. and Wilson, D. V. (1997). Self-similarity through high variability: statistical analysis of Ethernet LAN traffic at the source level. IEEE/ACM Trans. Networking 5, 7186.CrossRefGoogle Scholar
[47] Wischik, D. J. (2001). Sample path large deviations for queues with many inputs. To appear in Queueing Systems.Google Scholar
[48] Zwart, A. P., Borst, S. C. and Mandjes, M. (2000). Exact queueing asymptotics for multiple long-tailed on–off sources. Tech. Rept SPOR 2000-14, Eindhoven University of Technology.Google Scholar