Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-27T22:01:00.197Z Has data issue: false hasContentIssue false

Asymptotic Fluid Optimality and Efficiency of the Tracking Policy for Bandwidth-Sharing Networks

Published online by Cambridge University Press:  14 July 2016

Konstantin Avrachenkov*
Affiliation:
INRIA
Alexey Piunovskiy*
Affiliation:
INRIA
Yi Zhang*
Affiliation:
University of Liverpool
*
Postal address: INRIA, MAESTRO Team, 2004 Route des Lucioles - BP 93 FR-06902 Sophia Antipolis Cedex, France. Email address: [email protected]
∗∗Postal address: Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.
∗∗Postal address: Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Optimal control of stochastic bandwidth-sharing networks is typically difficult. In order to facilitate the analysis, deterministic analogues of stochastic bandwidth-sharing networks, the so-called fluid models, are often taken for analysis, as their optimal control can be found more easily. The tracking policy translates the fluid optimal control policy back to a control policy for the stochastic model, so that the fluid optimality can be achieved asymptotically when the stochastic model is scaled properly. In this work we study the efficiency of the tracking policy, that is, how fast the fluid optimality can be achieved in the stochastic model with respect to the scaling parameter. In particular, our result shows that, under certain conditions, the tracking policy can be as efficient as feedback policies.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2011 

References

[1] Anderson, W. J. (1991). Continuous-Time Markov Chains. Springer, New York.CrossRefGoogle Scholar
[2] Bäuerle, N. (2000). Asymptotic optimality of tracking policies in stochastic networks. Ann. Appl. Prob. 10, 10651083.Google Scholar
[3] Cantrell, P. (1986). Computation of the transient {M/M/1} queue CDF, PDF, and mean with generalized Q-functions. IEEE Trans. Commun. 34, 814817.Google Scholar
[4] Chen, H. (1996). Rate of convergence of the fluid approximation for generalized Jackson networks. J. Appl. Prob. 33, 804814.Google Scholar
[5] Chen, H. and Mandelbaum, A. (1991). Discrete flow networks: bottleneck analysis and fluid approximations. Math. Operat. Res. 16, 408446.Google Scholar
[6] Chen, H. and Mandelbaum, A. (1994). Hierachical modeling of stochastic networks. Part {I}. Fluid models. In Stochastic Modeling and Analysis of Manufacturing Systems, ed. Yao, D., Springer, New York, pp. 47105.Google Scholar
[7] Dai, J. G. (1995). On positive Harris recurrence of multiclass queueing networks: a unified approach via fluid limit models. Ann. Appl. Prob. 5, 4977.Google Scholar
[8] Gajrat, A. and Hordijk, A. (2000). Fluid approximation of a controlled multiclass tandem network. Queueing Systems 35, 349380.CrossRefGoogle Scholar
[9] Gajrat, A., Hordijk, A. and Ridder, A. (2003). Large-deviations analysis of the fluid approximation for a controllable tandem queue. Ann. Appl. Prob. 13, 14231448.Google Scholar
[10] Gleissner, W. (1988). The spread of epidemics. Appl. Math. Comput. 27, 167171.Google Scholar
[11] Guo, X. and Hernández-Lerma, O. (2009). Continuous-Time Markov Decision Processes. Springer, Berlin.Google Scholar
[12] Hernández-Lerma, O. (1994). Lectures on Continuous-Time Markov Control Processes. Sociedad Matemática Mexicana, Mexico City.Google Scholar
[13] Maglaras, C. (2000). Discrete-review policies for scheduling stochastic networks: trajectory tracking and fluid-scale asymptotic optimality. Ann. Appl. Prob. 10, 897929.CrossRefGoogle Scholar
[14] Mandelbaum, A. and Pats, G. (1995). State-dependent queues: approximations and applications. In Stochastic Networks (IMA Vol. Math. Appl. 71), eds Kelly, F. and Williams, R., Springer, New York, pp. 239282.Google Scholar
[15] Massoulié, L. and Roberts, J. W. (2000). Bandwidth sharing and admission control for elastic traffic. Telecommun. Systems 15, 185201.Google Scholar
[16] Pang, G. and Day, M. V. (2007). Fluid limits of optimally controlled queueing networks. J. Appl. Math. Stoch. Anal. 2007, 19 pp.Google Scholar
[17] Piunovskiy, A. (2009). Controlled Jump Markov processes with local transitions and their fluid approximation. WSEAS Trans. Systems Control 4, 399412.Google Scholar
[18] Piunovskiy, A. B. (2009). Random walk, birth-and-death process and their fluid approximations: absorbing case. Math. Meth. Operat. Res. 70, 285312.Google Scholar
[19] Piunovskiy, A. B. and Clancy, D. (2008). An explicit optimal intervention policy for a determinisitc epidemic model. Optimal Control Appl. Meth. 29, 413428.Google Scholar
[20] Piunovskiy, A. and Zhang, Y. (2011). Accuracy of fluid approximations to controlled birth-and-death processes: absorbing case. Math. Meth. Operat. Res., 29 pp.Google Scholar
[21] Piunovskiy, A. and Zhang, Y. (2011). On the fluid approximations of a class of general inventory level-dependent EOQ and EPQ models. To appear in Adv. Operat. Res. Google Scholar
[22] Pullan, M. C. (1995). Forms of optimal solutions for separated continuous linear programs. SIAM J. Control. Optimization 33, 19521952.Google Scholar
[23] Revuz, D. and Yor, M. (1999). Continuous Martingales and Brownian Motion, 3rd edn. Springer, Berlin.Google Scholar
[24] Robert, P. (2003). Stochastic Networks and Queues (Appl. Math. 52). Springer, Berlin.Google Scholar
[25] Roberts, J. and Massoulie, L. (1998). Bandwidth sharing and admission control for elastic traffic. In Proc. of ITC Specialist Seminar, Yokohama, Japan, pp. 185201.Google Scholar
[26] Sharma, O. P. and Tarabia, A. M. K. (2000). A simple transient analysis of an {M/M/1/N} queue. Sankhyā A} 62, 273281.Google Scholar
[27] Verloop, I. M. (2009). Scheduling in Stochastic Resource-Sharing Systems. , Eindhoven University of Technology.Google Scholar
[28] Verloop, I. M. and Núñez-Queija, R. (2009). Assessing the efficiency of resource allocations in bandwidth-sharing networks. Performance Evaluation 66, 5977.CrossRefGoogle Scholar
[29] Ye, L., Guo, X. and Hernández-Lerma, O. (2008). Existence and regularity of a nonhomogeneous transition matrix under measurability conditions. J. Theoret. Prob. 21, 604627.CrossRefGoogle Scholar
[30] Zhang, J. and Coyle, E. J. Jr. (1991). The transient solution of time-dependent {M/M/1} queues. IEEE Trans. Inf. Theory 37, 16901696.Google Scholar