As the size and complexity of simulation exercises in the military training industry increase, traditional monolithic simulation training systems struggle to scale efficiently. While most legacy simulations were designed to support interoperability amongst multiple Live, Virtual and Constructive (LVC) training systems, adding training elements to a large distributed Integrated Training Environments (ITE) exercises have traditionally required purchasing and configuring new hardware and/or software, as well as lead time of 120+ days to prepare for exercises. This approach has proven with large capital expenditures into hardware and software required for large-scale exercises that skew innovation to cost ratios.
Constructive Simulation systems have been used to provide efficient computer-generated forces in large scale distributed exercises, such as the Army’s One Semi-Automated Forces (OneSAF). OneSAF was developed to be a monolithic simulation system that does not support distributed multi-node and multi-core configuration and execution. Scaling large LVC exercise with multiple OneSAF instances at multiple locations with increasing cyber security requirements remain a challenge. With the US GovCloud reaching maturity and security needed for military training exercises, it is a prime time to examine the future of scalable constructive simulations to support the PEO STRI Synthetic Training Environment (STE) architecture goals.
Our research and development effort compares the traditional scalability approaches of monolithic simulations with that of inherent scalability available within cloud-based solutions. We examine legacy simulations using OneSAF as a testbed, based on current acquisition processes to assess its ability to meet evolving LVC training goals. The result of this study will highlight potential configurations incorporating horizontal and vertical scaling technologies to analyze management costs and performance differentiators to the overall STE architecture, while prototyping the appropriate architecture to meet modern simulation and training requirements.