Simulation Model, Warm-up Period, and Simulation Length of Cellular Systems

Kolahi, Samad S.

doi:10.1109/isms.2011.63

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…The bit sequence for this simulation is 0010111100101, and the simulation parameters from Table 1 are applied. The first two symbol duration slots represent the warm-up period (42 ) for which the system is unstable. Hence, the simulation starts after this warm-up period.…”

Section: Resultsmentioning

confidence: 99%

Engineering genetic circuits in receiver cells for diffusion-based molecular data communications

Durmaz

Tulluk

Aksoy

et al. 2023

Preprint

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Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules is challenging. Diffusing molecules may fall behind their schedule to arrive at a receiver, interfering with symbols of subsequent time slots and distorting the signal. Theoretical molecular communication models often focus solely on the characteristics of the communication channel and fail to provide an end-to-end system response, since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. There is a need to develop viable end-to-end communication models. In this paper, we present a model-based framework for designing diffusion-based molecular communication systems coupled with synthetic genetic circuits. We describe a novel approach to encode information as a sequence of bits, each transmitted from a sender as a burst of specific number of molecules, control cellular behavior, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. We also present Period Finder, as a tool to optimize communication parameters, including the number of molecules and symbol duration. This tool facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

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Section: Resultsmentioning

confidence: 99%

Engineering genetic circuits in receiver cells for diffusion-based molecular data communications

Durmaz

Tulluk

Aksoy

et al. 2023

Preprint

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“…However, with the benefits of using simulation comes downsides due to the rigidity of the software. The OEE data will show signs of a warm-up period before stabilising [24,25], which affects the overall OEE score. Yet, data analytics allows identification of the warm-up period, through line charts and thereafter removing the data before a stable chart is reached.…”

Section: Discussionmentioning

confidence: 99%

Combining Simulation and Data Analytics for OEE Improvement

Lindegren¹,

Lunau²,

Mafia³

et al. 2022

Int. j. simul. model.

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Overall Equipment Effectiveness (OEE) is a productivity performance metric widely used in industry to support production control decisions. However, there is still a gap in organisational procedures to systematically identify and address the most promising opportunities to improve the production setup. In this study, we propose and demonstrate a data-driven approach for increasing OEE by combining the strengths of discrete-event simulation with data analytics tools and methods, which provides a risk-free test environment that forms the basis for data-driven decisions and supports revealing production interdependencies. Therefore, this approach eases the process for practitioners to proactively identify production losses and forecast the outcome of the most promising selected improvement measures. A case study is performed to illustrate the potentialities of the proposed approach, demonstrating the interdependence between the processes and the improvement measures, and the knock-on effect both upstream and downstream. The results yield substantial insights and facilitate operational decisionmaking for managers.

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“…the request flow can be recurrent or represented by an arbitrary stochastic function. Examples of previous works addressing QS with warm-up are by Kolahi in [16] or by Kreinin in [17] for the characteristics of single channel QS, or by Bin Sun, A. N. Dudin in [1] studying the MAP/PH/n multichannel QS with warm-up and broadcasting service discipline. Mao and Humphrey in [18] examine the influence of the warm-up during virtual machine startup in the cloud system.…”

Section: Existing Modelsmentioning

confidence: 99%

Stochastic models for cloud computing performance evaluation

Хомоненко

Gindin

2014

Proceedings of the 10th Central and Eastern European Software Engineering Conference in Russia

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For cloud systems a set of stochastic models taking account of system context actualization is proposed. These models are based on queueing theory and extend its applications by studying the multichannel systems with warm-up and hyperexponential phasetype approximation of non-Markovian processes. Transition diagrams and matrices for the microstates of a sample cloud system are described and a scheme for computing the stationary probability distributions for requests number and waiting time is developed. The paper discusses the received computation results of the proposed modeling approach and their application to assessing the performance of the cloud systems.

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Simulation Model, Warm-up Period, and Simulation Length of Cellular Systems

Cited by 12 publications

References 16 publications

Engineering genetic circuits in receiver cells for diffusion-based molecular data communications

Engineering genetic circuits in receiver cells for diffusion-based molecular data communications

Combining Simulation and Data Analytics for OEE Improvement

Stochastic models for cloud computing performance evaluation

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