Semi-partitioned scheduling and task migration in dataflow networks

This thesis proposes design methodologies and techniques in the context of embedded computing systems. In particular, it focuses on embedded streaming systems, i.e., systems that process a continuous, possibly infinite stream of data from the environment. Typical examples of such systems are audio and video encoders and decoders. In order to achieve higher performance, nowadays embedded streaming systems are often implemented on execution platforms that contain multiple processors on a single chip. These execution platforms are called Multi-Processor Systems-on-Chip (MPSoCs). To exploit the parallelism available in MPSoCs, applications have to be decomposed in portions (also called tasks) that are inter-dependent, but can be executed in parallel. Each of these tasks is assigned to a certain processor of the system. This assignment of tasks to processors is called spatial scheduling of tasks, or task mapping. This thesis proposes techniques to optimize and adapt at run-time the mapping of tasks to processors, in order to achieve higher processor utilization, or energy efficiency, or to make the system fault tolerant.