Distributed real-time embedded (DRE) systems play a crucial role in many mission-critical applications such as disaster recovery command and control, power grid management, and agile manufacturing. These applications have stringent requirements for end-to-end timeliness and availability whose assurance is essential to their proper operation. In recent years, many DRE systems become open to the Internet and volatile physical environments where system workloads may vary significantly at run-time. Such systems require a paradigm shift from classical real-time computing to adaptive solutions that handle workload variations dynamically. This CAREER research develops adaptive Quality of Service control, a control-theoretic framework for adaptive DRE systems. This framework includes a suite of adaptive strategies and algorithms, formal models and analysis techniques, and a middleware architecture that integrates multiple adaptation strategies via distributed software feedback control loops. This research will significantly impact the field of DRE systems as it will enable DRE systems to achieve and maintain critical performance assurances despite dramatic changes in operational conditions and system failures. As a result, mission-critical DRE systems will be able to provide significantly more robust and reliable services in a broad range of highly unpredictable environments.
