Designing Predictable Time-Aware and Energy-Efficient Cyber-Physical Systems

Cyber-physical systems (CPS) involve the cyber components of computing and communication
interacting with and controlling elements in the physical world. Emerging CPS are increasingly distributed and perform coordinated sensing and actuation over large
geographical areas. Examples include local-scale industrial robots, city-scale traffic management, and regional/continental-scale smart grids. Hence, a hierarchy of resource constrained embedded sensing/actuation nodes, edge cloudlets and the cloud will be key to enable scalable coordination, while simultaneously hosting the intelligence behind these systems. To meet the low-latency real-time requirements of CPS, these platforms typically harness a variety of computing resources ranging from multi-core processors to hardware accelerators such as general-purpose Graphics-Processing Units (GP-GPUs).
In conjunction with low latency, a shared and precise notion of time is key to enabling coordinated action in distributed CPS. Hence, in this dissertation, we introduce abstractions, system-design methodologies and frameworks that enable time-based coordination in geo-distributed cyber-physical systems. While a shared notion of time enables coordination at the distributed scope, to coordinate effectively it is also necessary to simultaneously schedule multiple application components at the scope of each node, such that all deadlines are met, while ensuring that the resource/physical constraints of the system are satisfied. Therefore, this dissertation also introduces energy-, thermaland
resource-efficient analyzable real-time scheduling techniques for applications deployed on platforms utilizing both multi-core processors and hardware accelerators.
Our proposed solutions are readily applicable to commodity embedded, edge and cloud platforms, and together can enable time-aware and energy-efficient CPS.