A unified model for specification and simulation to design, acquire, and sustain a hybrid- or multi-cloud large scale data analytics system that balances cost, performance, modularity, and security.

SBIR topic DAF27BZ02-DV023 (U.S. Department of Defense – Air Force), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. Modern software-centric data systems across the Department of War operate in highly dynamic, shared cloud and hybrid infrastructure environments. Within complex weapon system sustainment platforms, such as the RSO CBM+ enterprise, accurately predicting and managing lifecycle resource consumption, computing performance, and ongoing operational costs presents a substantial operational challenge. Cloud and hybrid hosting costs fluctuate due to stochastic workload behaviors, data egress, and dynamic compute allocation. Across commercial and defense sectors, approximately 49% of organizations lack dedicated FinOps capabilities, resulting in frequent budget variances exceeding 18% and inefficient allocation of critical resources. Traditional MBSE methodologies and legacy toolchains (e.g., static Cameo models, detached MATLAB scripts, disconnected spreadsheets) lack the capability to execute high-fidelity, time-dependent simulations directly within the primary system specification model. This fragmentation creates significant lag between architectural planning, empirical software telemetry, and financial governance, severely limiting rapid system analysis and trade-space optimization. To address this capability gap, this project seeks innovative solutions that bridge DevOps, FinOps, and MBSE into a unified, executable environment. The proposed solution must demonstrate how system design specifications and executable simulation software can coexist within a cohesive architecture, such as an extensible, layered Service-Oriented Architecture (SOA) utilizing Systems Modeling Language (SysML) or equivalent open modeling frameworks. Offerors should propose methods to capture continuous empirical data from operational and developmental environments, allowing the model to dynamically update, calibrate, and predict…