Stochastic fluid flow models for determining optimal switching thresholds

Aggarwal, Vineet; Gautam, Natarajan; Kumara, Soundar; Greaves, Mark

doi:10.1016/j.peva.2004.06.002

Cited by 16 publications

(14 citation statements)

References 17 publications

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“…This is the probability that the buffer-2 content goes up from x * and reaches x * in time t. This is identical to the probability that the buffer content starting at zero goes up and comes back to zero within time t. The LST of G 43 t can be obtained by substituting appropriate terms in Aggarwal et al (2004) (see Equations (12), (13), and (14) there), given by…”

Section: Semi-markov Process Modelmentioning

confidence: 99%

“…In recent times, fluid models have been considered with varying service rates under some special conditions and circumstances. For example, Narayanan and Kulkarni (1996) analyze a multiclass fluid model that uses a static priority service policy, and Aggarwal et al (2004) consider a threshold-based policy where the processing rate is shared between two classes of fluid based on the amount of content waiting to be served. There are a few articles in multiclass fluid models that consider various policies.…”

Section: Introductionmentioning

confidence: 99%

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Resource-Sharing Queueing Systems with Fluid-Flow Traffic

Mahabhashyam

Gautam

Kumara

2008

Operations Research

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A system consisting of two buffers, each with independent fluid sources, is considered in this paper. Due to ease of implementation, the output capacities for the two buffers depend on the workload of only one of the buffers that is measured. A threshold-based policy is considered to dynamically assign output capacities for both buffers. Marginal workload distributions for the two buffers need to be evaluated for this policy. The key contribution of this paper is the performance analysis to derive the workload distribution in the two buffers. In addition, the paper also provides some guidelines to choose the output capacities for the two buffers as well as a mathematical program to determine an optimal threshold to dynamically switch between output capacities. Further, various applications of such systems to computer-communication networks are discussed.Subject classifications: probability: stochastic model applications; queues; information systems: management. Area of review: Stochastic Models.

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Section: Semi-markov Process Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource-Sharing Queueing Systems with Fluid-Flow Traffic

Mahabhashyam

Gautam

Kumara

2008

Operations Research

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“…Gautam et al [30] utilize dynamic programming to optimize the operation of a storage systems modeled as a Markov decision process. The fluid model has been used in other fields as production-inventory systems [31], or as computer and communication systems [32]. Then, in 2011 this method was used to evaluate the battery life [33] and, for modeling the operation of a battery systems, Jones et al and Ghiassi-Farrokhfal et al [27,28] use the network calculus method to model battery systems in smart grids.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Fluid Model Approach for the Sizing of Energy Storage in Wave-Wind Energy Systems

Domínguez-Navarro

Tedeschi

2016

Energies

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Abstract:The application of energy storage in offshore renewable generation systems allows managing the intrinsic uncertainty of the resources and improving the utilization factor of the electrical network. Optimal storage design algorithms generally have to evaluate the behavior of the whole system thousands times before converging to the optimal solution and the reliability of the results obviously depends on the quality of input data. On the other hand, the utilization of simplified storage models in the design stage can reduce the simulation time drastically, while still providing useful information. The goal of this paper is to evaluate the applicability of a methodology for sizing the energy storage system in a hybrid wind and wave farm, which is based on fluid models. The description and performance of this modeling approach will be introduced and compared to standard design procedures based on extensive simulations. Advantages and limitations of each approach will be underlined and the impact of input data quality will be discussed.

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“…Only a few articles consider varying output capacities, but most of them are to accommodate multi-class traffic. For example, Narayanan and Kulkarni [17] analyze a multi-class fluid model that uses a static-priority service policy and Agarwal et al [1] consider a threshold-based policy where the processing rate is shared between two classes of fluid based on the amount of content waiting to be served. However, they consider only two possible service capacities and the inputs are on-off CTMCs.…”

Section: Introductionmentioning

confidence: 99%

Obtaining Optimal Thresholds for Processors with Speed-Scaling

Polansky¹,

Sethuraman²,

Gautam³

2015

Electronic Notes in Theoretical Computer Science

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In this research we consider a processor that can operate at multiple speeds and suggest a strategy for optimal speed-scaling. While higher speeds improve latency, they also draw a lot of power. Thus we adopt a threshold-based policy that uses higher speeds under higher workload conditions, and vice versa. However, it is unclear how to select "optimal" thresholds. For that we use a stochastic fluid-flow model with varying processing speeds based on fluid level. First, given a set of thresholds, we develop an approach based on spectral expansion by modeling the evolution of the fluid queue as a semi-Markov process (SMP) and analyzing its performance. While there are techniques based on matrix-analytic methods and forward-backward decomposition, we show that they are not nearly as fast as the spectral-expansion SMP-based approach. Using the performance measures obtained from the SMP model, we suggest an algorithm for selecting the thresholds so that power consumption is minimized, while satisfying a quality-of-service constraint. We illustrate our results using a numerical example.

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Stochastic fluid flow models for determining optimal switching thresholds

Cited by 16 publications

References 17 publications

Resource-Sharing Queueing Systems with Fluid-Flow Traffic

Resource-Sharing Queueing Systems with Fluid-Flow Traffic

Evaluation of the Fluid Model Approach for the Sizing of Energy Storage in Wave-Wind Energy Systems

Obtaining Optimal Thresholds for Processors with Speed-Scaling

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