Möbius™ is a software tool for modeling the behavior of complex systems. Although it was originally developed for studying the reliability, availability, and performance of computer and network systems, its use has expanded rapidly. It is now used for a broad range of discrete-event systems, from biochemical reactions within genes to the effects of malicious attackers on secure computer systems, in addition to the original applications. That broad range of use is possible because of the flexibility and power found in Möbius, which come from its support of multiple high-level modeling formalisms and multiple solution techniques. This flexibility allows engineers and scientists to represent their systems in modeling languages appropriate to their problem domains, and then accurately and efficiently solve the systems using the solution techniques best suited to the systems’ size and complexity. Time- and space-efficient discrete-event simulation and numerical solution, based on compact MDD-based Markov processes, are both supported.

References in zbMATH (referenced in 25 articles )

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  1. Asadi, Ali Naghash; Azgomi, Mohammad Abdollahi; Entezari-Maleki, Reza: Model-based evaluation of the power versus performance of network routing algorithms (2021)
  2. Flammini, Francesco; Marrone, Stefano; Nardone, Roberto; Vittorini, Valeria: Compositional modeling of railway virtual coupling with stochastic activity networks (2021)
  3. Kolesnichenko, Anna; Senni, Valerio; Pourranjabar, Alireza; Remke, Anne: Applying mean-field approximation to continuous time Markov chains (2014)
  4. Heiner, Monika; Rohr, Christian; Schwarick, Martin: MARCIE -- model checking and reachability analysis done efficiently (2013) ioport
  5. Iacono, M.; Barbierato, E.; Gribaudo, M.: The simthesys multiformalism modeling framework (2012) ioport
  6. Babar, Junaid; Beccuti, Marco; Donatelli, Susanna; Miner, Andrew: GreatSPN enhanced with decision diagram data structures (2010) ioport
  7. Distefano, Salvatore: How to capture dynamic behaviours of dependable systems (2009)
  8. Nasri, Mitra; Shariati, Saeed; Azgomi, Mohammad Abdollahi: Performance modeling of a distributed web crawler using stochastic activity networks (2008)
  9. Zimmermann, Armin: Stochastic discrete event systems. Modeling, evaluation, applications. (2008)
  10. Kuntz, Matthias; Siegle, Markus: Symbolic model checking of stochastic systems: Theory and implementation (2006)
  11. Temsamani, Jamal; Carrasco, Juan A.: Transient analysis of Markov models of fault-tolerant systems with deferred repair using split regenerative randomization (2006)
  12. Ciardo, Gianfranco: Reachability set generation for Petri nets: Can brute force be smart? (2004)
  13. Kwiatkowska, Marta; Norman, Gethin; Parker, David: Probabilistic symbolic model checking with prism: a hybrid approach (2004) ioport
  14. Bohnenkamp, H.; Hermanns, H.; Katoen, J.-P.; Klaren, R.: The Modest modeling tool and its implementation. (2003) ioport
  15. Ciardo, G.; Jones, R. L.; Miner, A. S.; Siminiceanu, R.: Logical and stochastic modeling with Smart. (2003) ioport
  16. Derisavi, Salem; Hermanns, Holger; Sanders, William H.: Optimal state-space lumping in Markov chains (2003)
  17. Hermanns, Holger; Joubert, Christophe: A set of performance and dependability analysis components for CADP (2003)
  18. Derisavi, Salem; Kemper, Peter; Sanders, William H.; Courtney, Tod: The Möbius state-level abstract functional interface (2002)
  19. Gilmore, Stephen; Hillston, Jane; Ribaudo, Marina: PEPA nets: A structured performance modelling formalism (2002)
  20. Hopkins, Richard; King, Peter: A visual formalism for the composition of stochastic Petri nets (2002)

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Further publications can be found at: https://www.mobius.illinois.edu/papers.php