A fluid analysis framework for a Markovian process algebra
Theoretical Computer Science
On fluidization of discrete event models: observation and control of continuous Petri nets
Discrete Event Dynamic Systems
Fluid computation of the performance: energy tradeoff in large scale Markov models
ACM SIGMETRICS Performance Evaluation Review
Mean-field approximations for performance models with generally-timed transitions
ACM SIGMETRICS Performance Evaluation Review
Markovian agent modeling swarm intelligence algorithms in wireless sensor networks
Performance Evaluation
Mean-Field analysis of markov models with reward feedback
ASMTA'12 Proceedings of the 19th international conference on Analytical and Stochastic Modeling Techniques and Applications
PCTMC models of wireless sensor network protocols
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
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Fluid analysis of Population CTMCs with non-linear evolution rates requires moment closures to transform a linear system with infinitely many ordinary differential equations (ODEs) into a non-linear one with a finite number of ODEs. Due to the ubiquity of kinetics with quadratic rates in physical processes, various closure techniques have been discussed in the context of systems biology and performance analysis. However, little research effort has been put into moment closures for higher-order moments of models with piecewise linear and higher-order polynomial evolution rates. In this paper, we investigate moment closure techniques applied to such models. In particular we look at moment closures based on normal and log-normal distributions. We compare the accuracy of the moment approximating ODEs with the exact results obtained from simulations. We confirm that by incorporating higher-order moment ODEs, the moment closure techniques give accurate approximations to the standard deviation of populations. Moreover, they often improve the accuracy of mean approximations over the traditional mean-field techniques.