CTL Model Checking in the Cloud Using MapReduce
The recent extensive availability of "big data" platforms calls for a widespread adoption by the formal verification community. Cloud computing platforms represent a great opportunity to run massively parallel jobs, yet classical formal verification tools/techniques must undergo a deep tec...
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| Veröffentlicht in: | 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing S. 333 - 340 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.09.2014
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| Schlagworte: | |
| ISBN: | 9781479984473, 1479984477 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The recent extensive availability of "big data" platforms calls for a widespread adoption by the formal verification community. Cloud computing platforms represent a great opportunity to run massively parallel jobs, yet classical formal verification tools/techniques must undergo a deep technological transformation in order to exploit the new available architectures. This has raised an increasing interest in deploying verification techniques on parallel/distributed frameworks. In this paper we introduce a framework to ease the adoption of a distributed approach to verification of Computation Tree Logic (CTL) formulas on very large state spaces. The approach exploits/integrates a recently developed, parametric state-space builder. The whole framework adopts M AP R EDUCE as core computational model, and can be tailored to different modelling formalisms. The outcomes of several tests performed on (Petri-nets based) benchmark specifications are presented, thus showing the convenience of the proposed approach. |
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| ISBN: | 9781479984473 1479984477 |
| DOI: | 10.1109/SYNASC.2014.52 |

