A unified cloud-enabled discrete event parallel and distributed simulation architecture

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dc.contributor.authorRisco Martín, José Luis
dc.contributor.authorHenares Vilaboa, Kevin
dc.contributor.authorMittal, Saurabh
dc.contributor.authorAlmendras Aruzamen, Luis Fernando
dc.contributor.authorOlcoz Herrero, Katzalin
dc.date.accessioned2023-06-22T10:45:28Z
dc.date.available2023-06-22T10:45:28Z
dc.date.issued2022-07
dc.description©2022 Elsevier This project has been partially supported by the Education and Research Council of the Community of Madrid (Spain), under research grant S2018/TCS- 4423, and by the Google Cloud Research Credits program with the award GCP19980904.
dc.description.abstractCloud infrastructure provides rapid resource provision for on-demand computational require-ments. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M & S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M & S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M & S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M & S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M & S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95x and 1.84x, respectively.
dc.description.departmentSección Deptal. de Arquitectura de Computadores y Automática (Físicas)
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipComunidad de Madrid
dc.description.sponsorshipGoogle Cloud Research Credits
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/72754
dc.identifier.doi10.1016/j.simpat.2022.102539
dc.identifier.issn1569-190X
dc.identifier.officialurlhttp://dx.doi.org/10.1016/j.simpat.2022.102539
dc.identifier.relatedurlhttps://www.sciencedirect.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/71601
dc.journal.titleSimulation modelling practice and theory
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDCABAHLA-CM (S2018/TCS- 4423)
dc.relation.projectIDGCP1998090
dc.rights.accessRightsopen access
dc.subject.cdu004.8
dc.subject.keywordFramework
dc.subject.keywordDiscrete-event simulation
dc.subject.keywordParallel simulation
dc.subject.keywordDistributed simulation
dc.subject.keywordHigh performance computing
dc.subject.keywordCloud computing
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203.04 Inteligencia Artificial
dc.titleA unified cloud-enabled discrete event parallel and distributed simulation architecture
dc.typejournal article
dc.volume.number118
dspace.entity.typePublication
relation.isAuthorOfPublicationb18c2bd8-52be-4d79-bd8b-dbd8e970d703
relation.isAuthorOfPublication043fa854-a8f4-47c5-922e-9002f420ebac
relation.isAuthorOfPublication8cfc18ec-4816-404d-982d-21dc07318c07
relation.isAuthorOfPublication.latestForDiscoveryb18c2bd8-52be-4d79-bd8b-dbd8e970d703
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