3 research outputs found

    SDN4CoRE: A Simulation Model for Software-Defined Networking for Communication over Real-Time Ethernet

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    Ethernet has become the next standard for automotive and industrial automation networks. Standard extensions such as IEEE 802.1Q Time-Sensitive Networking (TSN) have been proven to meet the real-time and robustness requirements of these environments. Augmenting the TSN switching by Software-Defined Networking functions promises additional benefits: A programming option for TSN devices can add much value to the resilience, security, and adaptivity of the environment. Network simulation allows to model highly complex networks before assembly and is an essential process for the design and validation of future networks. Still, a simulation environment that supports programmable real-time networks is missing. This paper fills the gap by sharing our simulation model for Software-Defined Networking for Communication over Real-Time Ethernet (SDN4CoRE) and present initial results in modeling programmable real-time networks. In a case study, we show that SDN4CoRE can simulate complex programmable real-time networks and allows for testing and verifying the programming of real-time devices.Comment: If you cite this paper, please use the original reference: T. H\"ackel, P. Meyer, F. Korf, and T. C. Schmidt. SDN4CoRE: A Simulation Model for Software-Defined Networking for Communication over Real-Time Ethernet. In: Proceedings of the 6th International OMNeT++ Community Summit. September, 2019, Easychai

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
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