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OPTIMAL ROUTING IN OUTPUT-QUEUED FLEXIBLE SERVER SYSTEMS

Published online by Cambridge University Press:  23 March 2005

Alexander L. Stolyar
Affiliation:
Bell Labs, Lucent Technologies, Murray Hill, New Jersey 07974, stolyar@research.bell-labs.com

Abstract

We consider a queuing system with multitype customers and nonhomogeneous flexible servers, in the heavy traffic asymptotic regime and under a complete resource pooling (CRP) condition. For the input-queued (IQ) version of such a system (with customers being queued at the system “entrance,” one queue per each type), it was shown in the work of Mandelbaum and Stolyar that a simple parsimonious Gcμ scheduling rule is optimal in that it asymptotically minimizes the system customer workload and some strictly convex queuing costs. In this article, we consider a different—output-queued (OQ)—version of the model, where each arriving customer must be assigned to one of the servers immediately upon arrival. (This constraint can be interpreted as immediate routing of each customer to one of the “output queues,” one queue per each server.) Consequently, the space of controls allowed for an OQ system is a subset of that for the corresponding IQ system.

We introduce the MinDrift routing rule for OQ systems (which is as simple and parsimonious as Gcμ) and show that this rule, in conjunction with arbitrary work-conserving disciplines at the servers, has asymptotic optimality properties analogous to those Gcμ rule has for IQ systems. A key element of the analysis is the notion of system server workload, which, in particular, majorizes customer workload. We show that (1) the MinDrift rule asymptotically minimizes server workload process among all OQ-system disciplines and (2) this minimal process matches the minimal possible customer workload process in the corresponding IQ system. As a corollary, MinDrift asymptotically minimizes customer workload among all disciplines in either the OQ or IQ system.

Type
Research Article
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Andrews, M., Kumaran, K., Ramanan, K., Stolyar, A.L., Vijayakumar, R., & Whiting, P. (2004). Scheduling in a queueing system with asynchronously varying service rates. Probability in the Engineering and Informational Sciences 18: 191217.Google Scholar
Armony, M. & Bambos, N. (1999). Queueing networks with interacting service resources. In Proceedings of the 37th Annual Allerton Conference, pp. 4252.
Bell, S.L. & Williams, R.J. (2001). Dynamic scheduling of a system with two parallel servers in heavy traffic with complete resource pooling: Asymptotic optimality of a continuous review threshold policy. Annals of Applied Probability 11: 608649.Google Scholar
Bramson, M. (1998). State space collapse with applications to heavy traffic limits for multiclass queueing networks. Queueing Systems 30: 89148.Google Scholar
Chen, H. & Mandelbaum, A. (1991). Leontief Systems, RBV's and RBM's. In M.H.A. Davis & R.J. Elliott (eds.), Applied stochastic analysis. Gordon and Breach Science, pp. 143.
Dai, J.G. & Prabhakar, B. (2000). The throughput of data switches with and without speedup. In Proceedings of the INFOCOM'2000, pp. 556564.
Ethier, S.N. & Kurtz, T.G. (1986). Markov process: Characterization and convergence. New York: John Wiley & Sons.
Harrison, J.M. (1998). Heavy traffic analysis of a system with parallel servers: Asymptotic optimality of discrete review policies. Annals of Applied Probability 8: 822848.Google Scholar
Harrison, J.M. & Lopez, M.J. (1999). Heavy traffic resource pooling in parallel-server systems. Queueing Systems 33: 339368.Google Scholar
Kelly, F.P. & Laws, C.N. (1993). Dynamic routing in open queueing networks: Brownian models, cut constraints and resource pooling. Queueing Systems 13: 4786.Google Scholar
Laws, C.N. (1992). Resource pooling in queueing networks with dynamic routing. Advances in Applied Probability 24: 699726.Google Scholar
Mandelbaum, A. & Stolyar, A.L. (2004). Scheduling flexible servers with convex delay costs: Heavy-traffic optimality of the generalized cμ-rule. Operations Research 52(6): 836855.Google Scholar
McKeown, N., Anantharam, V., & Walrand, J. (1996). Achieving 100% throughput in an input-queued switch. In Proceedings of the INFOCOM'96, pp. 296302.
Stolyar, A.L. (2004). MaxWeight scheduling in a generalized switch: State space collapse and workload minimization in heavy traffic. Annals of Applied Probability 14(1): 153.Google Scholar
Tassiulas, L. & Ephremides, A. (1992). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio network. IEEE Transactions on Automatic Control 37: 19361948.Google Scholar
Teh, Y. & Ward, A. (2002). Critical thresholds for dynamic routing in queueing networks. Queueing Systems 42: 297316.Google Scholar
Van Mieghem, J.A. (1995). Dynamic scheduling with convex delay costs: The generalized cμ rule. Annals of Applied Probability 5: 809833.Google Scholar
Williams, R.J. (1998). An invariance principle for semimartingale reflecting Brownian motions in an orthant. Queueing Systems 30: 525.Google Scholar
Williams, R.J. (1998). Diffusion approximations for open multiclass queueing networks: Sufficient conditions involving state space collapse. Queueing Systems 30: 2788.Google Scholar
Williams, R.J. (2000). On dynamic scheduling of a parallel server system with complete resource pooling. Fields Institute Communications 28: 4971.Google Scholar