Abstract
Nowadays Discrete Event Systems (DESs) require complex and large models, for which distributed simulation engines become, in practice, the tools used to understand and analyse their behaviour. In this context, we have proposed a methodology based on Petri Nets (PNs) covering the phases from the modelling of the DES to the distributed simulation of the PN. The efficiency of the distributed simulation of these large-scale models is strongly dependent on the generation of initial partitions where the workload of the parts is well balanced among the individual simulation engines deployed. In the cloud the resources to support the simulation are provided in a flexible way using its own load balancing and migration mechanisms. Nevertheless, the distributed simulation of large DESs requires its own metrics to define the workload and mechanisms for load balancing. This divergence in concepts and mechanisms poses a significant difficulty in adopting the cloud for simulation, especially when computation and communication come at a cost. This paper revisits the basic principles of a distributed simulation of DESs models, and presents the first experimental results of a framework for simulating large scale timed PN models in a mini cluster as the necessary previous experimental work to large scale simulations on the cloud.
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Acknowledgments
This work was co-financed by the Aragonese Government and the European Regional Development Fund “Construyendo Europa desde Aragón" (COSMOS research group); and by the Spanish program “Programa estatal del Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i”, project PGC2018-099815-B-100. We thank Carlos Gracia for assistance in designing and constructing the Raspberry Pi mini cluster.
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Hodgetts, P. et al. (2021). Workload Evaluation in Distributed Simulation of DESs. In: Tserpes, K., et al. Economics of Grids, Clouds, Systems, and Services. GECON 2021. Lecture Notes in Computer Science(), vol 13072. Springer, Cham. https://doi.org/10.1007/978-3-030-92916-9_1
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