Model-driven Automated Deployment of Large-scale CPS Co-simulations in the Cloud

TitleModel-driven Automated Deployment of Large-scale CPS Co-simulations in the Cloud
Publication TypeConference Proceedings
Year of Conference2017
AuthorsBarve, Y. D., H. Neema, A. S. Gokhale, and J. Sztipanovits
Conference Name MODELS 2017 Satellite Event: Posters co-located with {ACM/IEEE} 20th International Conference on Model Driven Engineering Languages and Systems
Pagination463–464
Date Published09/2017
Conference LocationAustin,TX,USA
Abstract

With increasing advances in Internet-enabled devices, large cyber-physical
systems (CPS) are being realized by integrating several sub-systems
together. Analyzing and reasoning different properties of such CPS requires
co-simulations by composing individual and heterogeneous simulators,
each of which addresses only certain aspects of the CPS. Often these
co-simulations are realized as point solutions or composed in an ad
hoc manner, which makes it hard to reuse, maintain and evolve these
co-simulations. Although our prior work on a model-based framework
called Command and Control Wind Tunnel (C2WT) supports distributed
co-simulations, many challenges remain unresolved. For instance,
evaluating these complex CPSs requires large amount of computational
and I/O resources for which the cloud is an attractive option yet
there is a general lack of scientific approaches to deploy
co-simulations in the cloud. In this context, the key challenges
include (i) rapid provisioning and de-provisioning of experimental
resources in the cloud for different co-simulation workloads, (ii)
simulating incompatibility and resource violations, (iii) reliable
execution of co-simulation experiments, and (iv) reproducible
experiments. Our solution builds upon the C2WT heterogeneous
simulation integration technology and leverages the Docker container
technology to provide a model-driven integrated tool-suite for
specifying experiment and resource requirements, and deploying
repeatable cloud-scale experiments. In this work, we present the core
concepts and architecture of our framework, and provide a summary of
our current work in addressing these challenges.

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