Intelligent Dynamic Data Logging

Data logging of complex simulations can easily overwhelm computer resources or risk losing critical data. Knowing what data is most relevant while simultaneously minimizing collection resources, is extremely challenging.Intelligent Dynamic Data Logging (IDDL) senses run-time data volume, considers scenario objectives, and monitors system performance to record only the most relevant data. It maximizes the value of data collected while minimizing the data collection impact to processing, memory, network bandwidth, and persistent storage. This project establishes an IDDL framework that:1. Provides a process whereby users can prioritize data types with a dependency on objectives, specific conditions or scenarios;2. Defines and collects computer performance metrics that impact data logging capacity;3. Includes algorithms that intelligently prioritize data logging dynamically during the simulation and uses machine learning to predict and adapt data logging; and4. Architects, prototypes, and validates a data capture platform-as-a-service thato captures only the most important data at the right time while considering scenarios and real-time performance metrics ando adaptively and proactively captures unexpected high-priority data through machine learning.Intelligent Dynamic Data Logging increases scenario reliability while assuring the most relevant data is always available.Approved for Public Release | 17-MDA-9395(24 Oct 17)