Multicore platforms have the potential of revolutionizing the capabilities of embedded cyber-physical systems. Unfortunately, when such systems have safety-critical components, multicore platforms are rarely used. The reason is a lack of predictability associated with hardware components such as caches, memory controllers, etc., that are shared among cores. With current technology, very conservative estimates concerning the usage of these shared resources must be made, to certify that overuse violations do not occur at runtime. The resulting over-provisioning can be significant, easily negating the processing power of any additional cores. The goal of this project is to resolve this multicore "predictability problem" by developing allocation mechanisms that enable shared hardware resources to be controlled in a predictable way. The research agenda in this project includes fundamental research on relevant real-time resource allocation problems, prototyping efforts involving real-time operating systems and middleware, and experimental evaluations of improvements enabled by the developed mechanisms in timing analysis tools (which are used to determine task execution-time budgets). Addressing the "predictability problem" associated with multicore platforms would be a breakthrough result for safety-critical, cyber-physical systems in domains such as avionics and automobiles. When using multicore platforms to host highly-critical workloads in these domains, the current state of the art is to obviate the predictability problem by turning off all but one core. Unless a more intelligent solution can be found, such domains will not benefit from savings in size, weight, and power (SWaP) and gains in functionality that multicore platforms afford. Broader impacts include joint research with industry colleagues on supporting real-time workloads in unmanned air vehicles, the development of publicly-available open-source software that can be used by other institutions for research and teaching purposes, and the development of a new course on cyber-physical systems.

Trustworthy operation of next-generation complex power grid critical infrastructures requires mathematical and practical verification solutions to guarantee the correct infrastructural functionalities. This project develops the foundations of theoretical modeling, synthesis and real-world deployment of a formal and scalable controller code verifier for programmable logic controllers (PLCs) in cyber-physical settings. PLCs are widely used for control automation in industrial control systems. A PLC is typically connected to an engineering workstation where engineers develop the control logic to process the input values from sensors and issue control commands to actuators. The project focuses on protecting infrastructures against malicious control injection attacks on PLCs, such as Stuxnet, that inject malicious code on the device to drive the underlying physical platform to an unsafe state. The broader impact of this proposal is highly significant. It offers potential for real-time security for critical infrastructure systems covering sectors such as energy and manufacturing. The project's intellectual merit is in providing a mathematical and practical verification framework for cyber-physical systems through integration of offline formal methods, online monitoring solutions, and power systems analysis. Offline formal methods do not scale for large-scale platforms due to their exhaustive safety analysis of all possible system states, while online monitoring often reports findings too late for preventative action. This project takes a hybrid approach that dynamically predicts the possible next security incidents and reports to operators before an unsafe state is encountered, allowing time for response. The broader impact of this project is in providing practical mathematical analysis capabilities for general cyber-physical safety-critical infrastructure with potential direct impact on our national security. The research outcomes are integrated into education modules for graduate, undergraduate, and K-12 classrooms.