Model-driven Automated Deployment of Large-scale CPS Co-simulations in the Cloud Yogesh D. Barve, Himanshu Neema, Aniruddha Gokhale and Janos Sztipanovits Institute for Software-Integrated Systems, Dept. of EECS, Vanderbilt University, Nashville, TN 37212, USA Email:{yogesh.d.barve,himanshu.neema, a.gokhale, janos.sztipanovits}@vanderbilt.edu Abstract—With increasing advances in Internet-enabled de- vices, 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 experi- ment 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. Index Terms—co-simulations, verification, model driven, cloud I. INTRODUCTION AND PROBLEM STATEMENT Large-scale cyber physical systems (CPS) experiments are being increasingly deployed for real-world scenarios in do- mains such as building automation and control, smart power grid, health-care, and industrial processes. For example, power grid CPSs are composed of many multi-domain subsystems with different assets and technologies, such as electric grid, sensors, networking and physical control systems. Thus, de- signing and analyzing such complex systems needs extensive simulation and prototyping tools that span multiple domains. While recent advances in simulation tools have enabled modeling and simulation of system characteristics, a single simulator tool is not sufficient to model and experiment with CPS. This is due to the fact that no single simulator can simulate all aspects of CPS, and moreover, CPS require heterogeneous resources and execution environments. Thus co-simulation environments have emerged as an approach for modeling and simulating CPS. Co-simulation or coupled simu- lation is a methodology that focuses on evaluating the behavior of a system by integrating simulations of its components. Each specialized simulation tool can process and communicate var- ious events among participating simulation engines to model large-scale CPS. To realize such a co-simulation platform, proper time synchronization and coordination of message flows among participating simulations engines is needed. C2WT [1] is a heterogeneous simulation integration frame- work that we have previously developed at Vanderbilt Uni- versity. It enables model-based rapid synthesis of heteroge- neous and distributed CPS co-simulations. C2WT relies on the IEEE High-Level Architecture (HLA) standard. Domain- specific tools have been built on top of C2WT such as C2WT- TE [2] which targets transactive smart grid domain, and the SURE testbed [3] that targets security and resilience in CPS. Despite these advances, many challenges still remain unre- solved. For instance, large-scale simulations exhibit compute and/or I/O intensive workloads and may need large amount of such resources. Cloud computing can provide access to such a large pool of resources elastically and on-demand. However, existing cloud platforms lack tools for effective deployment of large-scale CPS simulations. Migrating existing simulation tools to the cloud is also a challenging task, which hinders the widespread adoption of cloud computing for CPS co-simulation. This problem is further exacerbated since CPS domain experts conducting the simulations often lack a proper understanding of the cloud resource provisioning and utilization thereby resulting in ad hoc and sub-optimal deployment of CPS simulations in the cloud. In this research, we focus primarily on cloud-based provi- sioning of large-scale CPS experiments, and outline the key challenges associated with deploying and experimenting with CPS co-simulations in the cloud. II. CHALLENGES IN REALIZING CLOUD- HOSTED CPS CO-SIMULATIONS The following challenges must be resolved to support reusable and extensible cloud-based CPS co-simulations. 1. Integrated tool to rapidly deploy experiments on cloud resources: To run experiments in the cloud, the framework should be able to acquire required resources, instantiate the deployment and execution of the co-simulation, and tear down