Person:
Olcoz Herrero, Katzalin

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First Name
Katzalin
Last Name
Olcoz Herrero
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Físicas
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
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UCM identifierORCIDScopus Author IDDialnet IDGoogle Scholar ID

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  • Publication
    A unified cloud-enabled discrete event parallel and distributed simulation architecture
    (Elsevier, 2022-07) Risco Martín, José Luis; Henares Vilaboa, Kevin; Mittal, Saurabh; Almendras Aruzamen, Luis Fernando; Olcoz Herrero, Katzalin
    Cloud 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.