Abstract
Performance evaluation plays an important role in system analysis. A compositional approach to model and evaluate content adaptation systems is presented in this paper, using the high-level system description and modelling language PEPA. Three computational methods, i.e. solving generator, stochastic simulation and fluid approximation, are demonstrated to derive performance measures from the PEPA model of content adaptation systems. A comprehensive comparison between these three computational methods shows that solving generator is only feasible for evaluating small scale systems, while for large scale systems the other two approaches are effective and promising. The simulation can achieve a high accuracy at high computational costs, while the fluid approximation approach can obtain an acceptable accuracy at a low cost. The consistence between the latter two computational methods is also studied.
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Notes
This kind of scenario or population is determined by the starting state of the PEPA model, which is the same as the starting states of the corresponding concentrated density dependent CTMCs.
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Acknowledgments
Part of this research has been carried out while J. Ding was at the University of Edinburgh, UK, with the School of Informatics and the School of Engineering. The author acknowledges the financial support by the National Natural Science Foundation of China under Grant No. 61301111 and No. 61472343, and the NSF of Jiangsu Province of China under Grant BK20140492. The author also acknowledges the support by SRF for ROCS, SEM, and Talent Project of New Century, Yangzhou University, as well as the Open Fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2015KF-05, Beihang University.
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Ding, J. A Comparison of Fluid Approximation and Stochastic Simulation for Evaluating Content Adaptation Systems. Wireless Pers Commun 84, 231–250 (2015). https://doi.org/10.1007/s11277-015-2605-x
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DOI: https://doi.org/10.1007/s11277-015-2605-x