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Activity title

Data Farming Services (DFS) for Analysis and Simulation-Based Decision Support

Activity Reference

MSG-155

Panel

NATO Modelling and Simulation Group

Security Classification

PUBLIC RELEASE

Status

Active

Activity type

RTG

Start date

2017

End date

2020

Keywords

Collaboration, Connected Forces, Cyber Defence, Data Farming, Decision Support, Modeling, MSG, MSG155, Resource Allocation, Simulation, Training Systems

Background

The methods and processes of Data Farming have been developed in the six areas of model development, rapid prototyping of scenarios, design of experiments, high performance computing, analysis and visualization of large simulation data output, and collaborative processes. These six domains of Data Farming have been documented as part of the work of the MSG-088 Task Group that codified the data farming concept. In addition, the follow-on MSG-124 Task Group turned the concept into actionable data farming decision support in part by developing a cyber simulation model and a decision support tool. These activities have proven Data Farming ready for application and implementation. Now Data Farming can be made accessible and usable by NATO through MSG-155 efforts to develop the groundwork for analysis and simulation-based decision support.

Objectives

Through co-operation among Alliance bodies, NATO member nations and partner nations participating in this task group, the overall goal is to establish the effective utilisation of data farming within appropriate areas of application using decision support tools for the ultimate purpose to assist NATO decision makers. The general objective of this Task Group is to extend data farming capability and accessibility through developing Data Farming Services (DFS) in accordance with the Modelling as a Service concept (NMSG 136) for analysis, wargaming, other simulation-based decision support, and training. The specific objective is to develop a road map for what needs to be done in order for NATO to provide DFS. This road map would show the way for developing a technical concept for DFS through an integrated toolset. Additionally, the work would produce and refine technical prototypes useful for implementation of the road map. DFS would support the application and execution of the Data Farming process as codified in MSG-088 and as applied in MSG-124 in a mature, productive and user-friendly way. The architecture of DFS would consider recommendations of the NATO MSG-136 Task Group “Modelling and Simulation as a Service” where appropriate. The intent is for DFS to support many different application use case areas for data farming. Some possible use cases, or branches emanating from the core data farming capabilities, are listed in the topics to be covered.

Topics

A common core for data farming services beyond that codified in MSG-088 and applied in MSG-124 would be developed. This common core allows processing M&S applications across the spectrum of NATO needs. The advanced simulation service capability includes developing the groundwork for DFS through efforts in each of the six data farming domains. In order to develop a Data Farming core service that will support the road map, this work is broken down into the separate data farming domains as follows: 1. Rapid Scenario Prototyping: Scenario definition and adaption may be supported independently of a specific simulation model by providing, for example, scenario editors supporting SISO-standards. 2. Model Development: The creation of models is performed outside of the core. DFS would provide generic I/O interfaces to support service based integration of existing models. 3. Design of Experiments (DOE): Different available DOE need to be available within the core toolset. In addition it might be valuable to support optimization approaches in conjunction with fixed DOEs. 4. High Performance Computing (HPC): The Data Farming core needs to provide a generic service approach to handle different HPC systems. 5. Analysis and Visualization: The DFTOP prototype of MSG-124 is to be integrated, enhanced and further developed as well as to implement new innovative approaches such as techniques of the big data computation area. 6. Collaboration: A process description for Data Farming experiments within M&S process and MaaS standards, e.g. DSEEP, needs to be developed.

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