Effect of model uncertainty on some optimal routing problems

Mohanty, B.P.; Cassandras, C.G.

doi:10.1007/bf00940712

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…Even if the exact solution cannot be obtained by such "lower-resolution" models, one can still obtain near-optimal 0018-9286/02$17.00 © 2002 IEEE points that exhibit robustness with respect to certain aspects of the model they are based on. Such observations have been made in several contexts (e.g., [12]), including recent results related to SFMs reported in [13] and [21] where a connection between the SFM and queuing-system-based solution is established for various optimization problems in queuing systems.…”

mentioning

confidence: 83%

“…However, Theorem 4 includes a direct proof of the unbiasedness of the estimator , whereas the present approach requires a separate proof that the sample derivative is in fact unbiased. The unbiasedness of this IPA derivative will be proven later, after we establish the IPA derivative of the work defined in (12). Theorem 6: For every ,…”

Section: B Ipa Using Sample Derivativesmentioning

confidence: 97%

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Perturbation analysis for online control and optimization of stochastic fluid models

Cassandras

Wardi

Melamed

et al. 2002

IEEE Trans. Automat. Contr.

165

163

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This paper uses stochastic fluid models (SFMs) for control and optimization (rather than performance analysis) of communication networks, focusing on problems of buffer control. We derive gradient estimators for packet loss and workload related performance metrics with respect to threshold parameters. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics, including traffic and processing rates (i.e., they are nonparametric). This renders them computable in online environments and easily implementable for network management and control. We further demonstrate their use in buffer control problems where our SFM-based estimators are evaluated based on data from an actual system. Index Terms-Communication network, perturbation analysis, stochastic fluid network.

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mentioning

confidence: 83%

Section: B Ipa Using Sample Derivativesmentioning

confidence: 97%

Perturbation analysis for online control and optimization of stochastic fluid models

Cassandras

Wardi

Melamed

et al. 2002

IEEE Trans. Automat. Contr.

165

163

View full text Add to dashboard Cite

show abstract

“…Even if the exact solution cannot be obtained by such "low-resolution" models, one can still obtain nearoptimal points that exhibit robustness with respect to certain aspects of the model they are based on. Such observations have been made in several contexts (e.g., [14]), including recent results related to SFMs reported in [15] where a connection between the SFM and queueing-system-based solution is established for various optimization problems in queueing systems.…”

mentioning

confidence: 83%

IPA for delay threshold violation using stochastic fluid models

Panayiotou

Cassandras

2008

2008 9th International Workshop on Discrete Event Systems

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In this paper we investigate the problem of delivering Quality of Service (QoS) guarantees in the context of communication networks, studied through stochastic fluid models. The paper follows the approach developed in [1], [2]. Its main contribution is that it develops Infinitesimal Perturbation Analysis (IPA) estimators of a cost function that also includes delay violation constraints, rather than the average delay that is typically investigated in [1], [2] and other similar work.

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“…It is clear from Steps 3.2) and 3.3) of the SPC procedure in the last section, that in order to continue sample path construction from state in the hypothetical sample path, we must have lifetimes (equivalently, occurrence times) for all events in the feasible event set . A key result from [5] is a necessary and sufficient condition for sample path construction: when event is observed in the actual DES, construction of the hypothetical sample path can continue if and only if (14) otherwise, construction of the hypothetical sample path is "suspended" in state until some future observed event causes (14) to be satisfied. Thus, with every observed event, condition (14) is checked: if it is satisfied, the SPC procedure is invoked to update the state of the constructed sample path; otherwise, construction is suspended at until some future observed event causes (14) to be satisfied.…”

Section: Concurrent Sample Path Constructionmentioning

confidence: 99%

“…A key result from [5] is a necessary and sufficient condition for sample path construction: when event is observed in the actual DES, construction of the hypothetical sample path can continue if and only if (14) otherwise, construction of the hypothetical sample path is "suspended" in state until some future observed event causes (14) to be satisfied. Thus, with every observed event, condition (14) is checked: if it is satisfied, the SPC procedure is invoked to update the state of the constructed sample path; otherwise, construction is suspended at until some future observed event causes (14) to be satisfied. Note that with every observed event the set is updated and possibly enlarged, while the set remains fixed, since it depends only on .…”

Section: Concurrent Sample Path Constructionmentioning

confidence: 99%

Design and implementation of an adaptive dispatching controller for elevator systems during uppeak traffic

Pepyne

Cassandras²

1998

IEEE Trans. Contr. Syst. Technol.

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We design a dispatching controller for elevator systems during uppeak passenger traffic with the ability to adapt to changing operating conditions. The design of this controller is motivated by our previous paper where we proved that for a queuing model of the uppeak dispatching problem a threshold policy is optimal (in the sense of minimizing the average passenger waiting time) with threshold parameters that depend on the passenger arrival rate. The controller, which we call the concurrent estimation dispatching algorithm (CEDA), uses concurrent estimation techniques for discrete-event systems. The CEDA allows us to observe the elevator system while it operates under some arbitrary thresholds, and concurrently estimate, in an unobtrusive way, what the waiting time would have been had the system operated under a set of different thresholds. These concurrently estimated waiting times are used to adapt the operating thresholds to match the elevator service rate to a changing passenger arrival rate. Implementation issues relating to the limited state information provided by actual elevator systems are resolved in a way that maintains modest computational requirements and avoids the need for supplemental sensors beyond those already typically provided. Numerical performance results show the advantages of the CEDA over currently used dispatching algorithms for uppeak.

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Effect of model uncertainty on some optimal routing problems

Cited by 15 publications

References 23 publications

Perturbation analysis for online control and optimization of stochastic fluid models

Perturbation analysis for online control and optimization of stochastic fluid models

IPA for delay threshold violation using stochastic fluid models

Design and implementation of an adaptive dispatching controller for elevator systems during uppeak traffic

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