Modeling and Simulation as a Service (MSaaS) embodies the idea that simulations should be composed quickly for the task at hand from loosely coupled shared components, simulation services, in a cloud-based environment. These simulations are then offered, as composed simulation services, to human and technical consumers. Instrumental to this, is functionality that lets a simulation operator discover and compose simulation services and execute the composition. We describe this functionality in terms of what we call MSaaS infrastructure capabilities. Following the idea of stepwise refinement, the discovery and composition of simulation services can be done at design time using implementation-independent information about simulation services and at implementation time using implementation-specific information about simulation services. The execution environment can also be set up at design time and at implementation time. We therefore describe the MSaaS infrastructure capabilities in terms of how they are used on both implementation-independent and implementation-specific service information. By doing these elaborations, we intend to gain greater insight into how to perform simulation service discovery, composition, and execution. We conclude that although much of the required functionality for a MSaaS infrastructure is available through existing platforms and frameworks, it is necessary to offer this functionality as services, alongside (composed) simulation services, to fulfill the MSaaS vision.

When technology opens up new domains or areas of research, such as human-agent teaming, new challenges in assessments emerge. Assessments may not be as systematically conducted as new measures develop, and the research may not be as firmly grounded in theory since theories in newer domains are still being formulated. As a result, research in these domains can be fragmented. To address these, an empirically-driven network approach that is complementary to the traditional theory-driven approach is proposed. The network approach seeks to discover patterns and structure in the assessment metadata (.e.g., constructs and measures) that can provide starting points and direction for future research. This paper outlines the workflow of the network approach which comprises three steps: (1) Data Preparation; (2) Data Analysis; and (3) Structure Discovery. As most of the work has been on Data Preparation, the paper will focus on the complexities and issues encountered in the first step, and include broad overviews of the subsequent steps. Anticipated use and outcomes of the network approach are also discussed.

Our research has included leveraging Virtualization Technologies to provide integration, configuration and execution relief of Modeling & Simulation (M&S) event planning, instantiation and analysis. We have achieved this through a single service that is used to deploy and execute stand-alone applications as well as separate, but cooperative, applications on a dynamic virtual machine-based cloud. This use of virtualization technology shows significant cost savings in reducing the human effort for integration, test and execution by providing a powerful virtual machine environment that combines new and existing applications and their configurations. Our effort eliminates the time needed to manually configure and execute these applications on physical hardware once they are captured in the system.

Interoperability among distributed models and simulations is complex, tedious and difficult to evaluate. Integrating models that were developed for various purposes with disparate technologies and managed by independent organizations is often the goal. This goal is underestimated due to misleading facts of commonalities between those applications. Common compliance with middleware architectures, modeling goals and even object models gives a false impression of complete interoperability. There are numerous considerations when developing a distributed simulation environment. The event's objectives drive the necessary simulation functions, but how those simulation functions interact needs to be meticulously designed for true interoperability. The semantics of the information transmitted, the behavior necessary across multiple applications, fidelity and resolution synchronization are only a subset of the systems engineering necessary for a coherent System of Systems. This paper covers interoperability complexities and proposes criteria to consider when developing, integrating and executing a distributed modeling and simulation architecture.

Designing a distributed simulation environment across multiple domains that typically have disparate middleware transport protocols, data exchange formats and applications increases the difficulty of capturing and linking system design decisions to the resultant implementation. Systems engineering efforts for distributed simulation environments are typically based on the middleware transport used, the applications available and the constraints placed on the technical team including network, computer and personnel limitations. To facilitate community re-use, systems engineering should focus on integrated operational function decomposition. This links data elements produced within the simulation to the functional capabilities required by the user. The system design should be captured at a functional level and subsequently linked to the technical design. Doing this within a data-driven systems engineering infrastructure allows generative programming techniques to assist accurate, flexible and rapid architecture development. This paper describes the MATREX program systems engineering process, infrastructure and path forward.

Designing a distributed simulation environment across multiple domains increases the difficulty of capturing and linking system design decisions to testable application specifications. Domains typically have disparate middleware transport protocols, data exchange formats and applications. Systems engineering products typically focused on distributed simulation environments are based on the middleware transport used, the applications available and the constraints placed on the technical team including network, computer and personnel limitations. This practice makes it difficult to link and re-use cross-domain systems because the integration point is the syntax rather than the functional interoperability or semantics. Systems engineering considerate of operational functions integration allows the linking of the data elements produced within the simulation to the functional capabilities required by the user. The system design should be captured at a functional level and subsequently linked to the technical design. This allows the functional requirements to be linked to system design and allocated to specific models. The low level requirements, object model and test cases can then be auto-generated based on model allocation to functions. The MATREX systems engineering product is data-driven, easing information maintenance duties by linking the products and simplifying the editing of the system design.