The innovation proposed is a framework for autonomous Traffic Flow Management (TFM) under Trajectory Based Operations (TBO) for Unmanned Aerial Systems (UAS). The concept, called DRIFT-UAS (Distributed Resilient Framework for Trajectory Management of Unmanned Aerial Systems), is a cloud-based system that consists of algorithms and an information-sharing framework that would enable autonomous trajectory planning and strategic deconflicting of trajectories of manned and unmanned aircraft, while optimizing system-wide objectives such as safety, efficiency, and equity. DRIFT-UAS envisions four information signals that are exchanged in a cloud-based environment. The signals are (a) trajectory intent from an aircraft to DRIFT-UAS, (b) trajectory feedback (e.g., level of congestion on the proposed route as well as nearby routes in time and space) from DRIFT-UAS to the aircraft (c) loading projections from DRIFT-UAS to NAS ATC resources, and (d) capacity signals derived from weather forecasts, dynamic airspace restrictions, or acceptable loading levels at various NAS resources. The signals are processed by a centralized MDM (Master De-conflicting Module) to generate a trajectory feedback signal, and ATGMs (Autonomous Trajectory Generation Modules) autonomously generate trajectories for aircraft based on the feedback signal. DRIFT-UAS is based on a new class of algorithms for solving large-scale TFM problems by separating TFM optimization into two problems---a master problem, equivalent to the MDM that checks for capacity violations and allocates resources to competing aircraft, and a sub-problem, equivalent to the ATGM solved by each individual aircraft that generates 4-d trajectories for each flight. The master problem exchanges dual prices that signal congestion across ATC resources to guide the sub-problems to an optimal solution.