The algorithmic technique of fixed-parameter analysis of computationally intractable problems seeks to obtain a deeper understanding of the underlying causes of the intractability, with a view to identifying conditions under which the problem becomes tractable. We apply fixed-parameter analysis to the fixed-priority and EDF scheduling of recurrent (periodic and sporadic) task systems upon preemptive uniprocessor platforms.

Autonomous systems are increasingly using components that incorporate machine learning and other AI-based techniques in order to achieve improved performance. We address the problem of assuring correctness in safety-critical systems that use such components. We investigate an approach which formulates the problem as one in which performance is an objective function to be optimized while safety is a hard constraint that must be satisfied. We then apply heuristics and algorithmic techniques from optimization theory in order to solve the resulting constrained optimization problem.

Schedulability analysis techniques that are well understood within the real-time scheduling community are applied to the analysis of recurrent real-time workloads that are modeled using the synchronous data-flow graph (SDFG) model. An enhancement to the standard SDFG model is proposed, that permits the specification of a real-time latency constraint between a specified input and a specified output of an SDFG. A technique is derived for transforming such an enhanced SDFG to a collection of traditional 3-parameter sporadic tasks, thereby allowing for the analysis of systems of SDFG tasks using the methods and algorithms that have previously been developed within the real-time scheduling community for the analysis of systems of such sporadic tasks. The applicability of this approach is illustrated by applying prior results from real-time scheduling theory to construct an exact preemptive uniprocessor schedulability test for collections of recurrent processes that are each represented using the enhanced SDFG model.

The global scheduling of systems that can be modeled as collections of multiple independent recurrent real-time tasks, each represented as a synchronous dataflow graph (SDFG), upon an identical multiprocessor platform is considered. An EDF-based scheduling algorithm is proved optimal under the speedup factor metric, and a speedup-optimal sufficient schedulability test is derived.