Analysing Dataflow Programs with Causation Traces
Michail Boulasikis, Flavius Gruian, Gareth Callanan, Jörn W. Janneck · 2022
Stream processing applications are naturally described as dataflow programs. Dataflow programs modelled as actor networks are well suited to describe concurrent and computationally intensive problems. Realistic dataflow programs are typically characterized by highly dynamic behaviour, limiting the applicability of static analysis techniques. In this work we explore using dynamic analyses of dataflow programs by making use of causation traces; graphs which capture instances of the program's execution. We outline how they can be used to inform pipelining and architectural decisions and conclude by delineating how this research can be expanded upon using multiple traces and doing more types of analyses.