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Im Rahmen des Projektes 'AgentWork' wird ein Workflow-Management-System entwickelt, das es ermöglicht, Workflows zur Laufzeit zu adaptieren, wenn aufgrund eines Anwendungsereignisses die logische Struktur des Workflows nicht mehr adäquat ist. Dazu ist es insbesondere erforderlich, solche Ereignisse zu erkennen, den betroffenen Workflow zu unterbrechen, zu adaptieren und anschließend den adaptierten Workflow weiter auszuführen. In der Arbeit wurden die Algorithmen für die Arbeit des sogenannten Adaptations-Agenten entwickelt, der die eigentlichen Adaptationen an nicht mehr adäquaten Workflows vornimmt. Dazu wird zuerst der Teil des Workflows bestimmt, der von einem Anwendungsereignis betroffen ist. Anschliessend wird dieser Teil durch Adaptationen des Kontroll- und Datenflusses an die veränderte Situation angepasst. Im Rahmen der Arbeit wurde auch eine prototypische Implementierung des Adaptations-Agenten erstellt.

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures.