The objective of this research is to conceptualize, implement, and evaluate a framework called Resilient Autonomic Software Systems RASS inspired by the MAPE-K i.e., Monitor, Analyze, Plan, and Execute based on Knowledge paradigm for autonomic systems aka self-managing systems. Sensor data is obtained from a variety of systems e.g., airplanes, UAVs, cloud systems, and cell-phones by the Monitor component of RASS. These data are aggregated and sent to the Analyze Adaptation component that determines whether self-adaptation is needed and why. The results of this analysis are passed to the Plan Adaptation component, which determines an optimal or near-optimal plan to automatically adapt the system. This plan is received by the Execution Adaptation component, which sends adaptation commands to the parts of the system that need to be adapted andor recovered, and orchestrates the adaptation to ensure an appropriate and consistent recovery. Because the adaptation plan is driven by the systems software architecture, an architecture discovery system called DeSARM recovers the descriptive architecture i.e., the one that reflects the actual system or supplements the prescriptive architecture i.e., the one used to guide the development when one is not available or has been eroded by system evolution. The Knowledge Base of RASS includes recovery methods and models that capture their performance overhead, failure recovery software patterns, adaptation patterns, a configuration database, an application binary repository to assist in recovering the software architecture of mobile applications, and the descriptive and prescriptive software architectures. A significant challenge of our research is that all the activities described above need to be carried out in a completely decentralized fashion. This is essential for achieving high-resiliency and high-dependability.