Erkennung und dynamische Ersetzung von Fragmenten in Workflow-Modellen

Das kontinuierliche Anwachsen der Vernetzung von beliebigen Dingen innerhalb des Internets, im Englischen „Internet of Things“ (IoT) genannt, versetzt die Anforderungen an die IT in neue Dimensionen. Geschäftsprozesse die sich im Bereich des IoT bewegen müssen mit komplexen Situationen in immer stärker vernetzten Umgebungen arbeiten und auf wechselnde Situationen reagieren. Ein Prozessmodell, dass zu automatisierten Ausführung eines Geschäftsprozesses eingesetzt wird, muss somit verschiedenste Situationen und de ren Abhängigkeiten berücksichtigen. Die Modellierung solcher Prozesse, entwickelt sich dadurch, zu einer unüberschaubaren, komplexen Aufgabe. Eine Möglichkeit im Bereich der Geschäftsprozesse, um auf verschiedene Situationen zu reagieren, ist die Verwendung von Prozessfragmenten. Prozessfragmente werden eingesetzt, um häufig auftretende Teile in Geschäftsprozessen für die Wiederverwendung zu modularisieren. Dieses Vorgehen erlaubt die dynamische Adaption der Prozesse zur Laufzeit, indem Prozessfragmente basierend auf vorliegenden Situationen ausgewählt werden, um eine bestimmte Geschäftsaktivität auszuführen. In dieser Arbeit werden Konzepte vorgestellt, welche die Entwicklung und das Ausführen von situationsadaptiven Prozessen unterstützt. So werden die Konzepte der ProSit- Entwicklungsmethode und des ProSit-Systems vorgestellt. Die ProSit-Entwicklungsmethode gibt einen Rahmen für die Entwicklung von situationsadaptiven Prozessen mithilfe von Prozessfragmenten. Dabei werden in der ProSit-Entwicklungsmethode Prozessteile erkannt die äquivalent zu den vorhanden Prozessfragmenten sind. Diese werden durch abstrakte Aktivitäten ersetzt, die anschliessend zur Laufzeit innerhalb des ProSit-Systems sich der vorliegenden Situation anpassen. Des Weiteren wird ein Verfahren auf Basis des TOSCA- Standards implementiert, um die verwendeten Prozessfragmente dynamisch zur Laufzeit in Cloud-Umgebungen bereitstellen zu können

Konzept und Implementierung eines Situation Handlers

Im Rahmen von Industrie 4.0 werden Konzepte zur Automatisierung von Industrieanlagen erforscht. Eine wichtige Rolle spielen dabei Sensoren zur Erkennung von Kontextinformationen und Workflows zur automatisierten Ausführung von Geschäftsprozessen. Um einen hohen Grad an Autonomie zu erreichen, müssen Workflows den von Sensoren erkannten Kontext berücksichtigen und angemessen auf gegenwärtige Situationen reagieren. Die Berücksichtigung von Kontextinformationen in Workflows macht deren Modellierung jedoch außerordentlich komplex, da durch die Kontextbehandlung sehr viele Situationen individuell behandelt werden müssen. Eine Möglichkeit zur Handhabung dieser Komplexität besteht in der Aufteilung von Workflows in Workflow-Fragmente, die in Abhängigkeit zur vorherrschenden Situation zur Ausführung einer bestimmten Aktivität ausgewählt werden. Dies ermöglicht die gezielte Modellierung von Workflow-Fragmenten, die für eine bestimmte Situation die jeweils am besten geeignete Lösung beschreiben. In dieser Arbeit wird ein Situation Handler zur Handhabung von Kontextinformationen entwickelt, der von Workflows aufgerufen werden kann, um automatisiert ein geeignetes Workflow-Fragment zur Ausführung einer bestimmten Aktivität auszuwählen. Dabei wird die Auswahl des Fragments von der gegenwärtigen Situation beeinflusst. Zur Erkennung von vorherrschenden Situationen wird ein Situation Recognition System eingesetzt, das feingranulare, technische Kontextinformationen zu höherwertigen Situationen abstrahiert. Das Situation Recognition System wurde im Rahmen eines Forschungsprojektes der Universität Stuttgart entwickelt. Es wird zudem der situationsabhängige Versand von Notifikationen unterstützt. Zum Versand wird ein Pluginsystem eingesetzt, das die Verwendung von verschiedenen Technologien zum Versand erlaubt

Dynamic process fragment injection in a service orchestration engine

The EU Project Allow Ensembles aims to develop a new design principle for large-scale collective systems (CAS) based on the concepts of cells and ensembles, where cells represent a concrete functionality in a system, and the ensembles are collections of cells which collaborate in order to fulfill a certain goal in a given context. Adaptive Pervasive Flows (APF) are based on workflow technology and utilized in pervasive environments to model the cell's behavior. During runtime, APF instances must be able to adapt their behavior, e.g. due to failures or changes in their environment, in order to be able to fulfill a certain goal. For this purpose, APFs may contain a particular type of activities, known as abstract activities. Abstract activities partially specify the flow behavior, which must then be resolved during runtime into one or multiple concrete activities, which in turn must be injected into the running flow. In the scope of this thesis, APFs are specified using an extension of the WS-BPEL language. The WS-BPEL language, provide the necessary mechanisms for extending the language for modeling custom process activities and specifying their behavior. In this thesis we focus on the WS-BPEL language and on an extended version of the Apache ODE orchestration engine, for business process modeling and execution respectively. With respect to the injection of pervasive process fragments, which contain one or multiple activities and properties, the language and execution engine requirements and constraints are investigated. For this purpose, a State-of-the-Art analysis on generic process fragment injection approaches is first driven. Once the constraints are detected, we present the formalization of the required language extension based on WS-BPEL language, and conduct a specification of requirements and architectural design of the final prototype based on the Apache Orchestration Director Engine (Apache ODE). For integration purposes in the overall required execution environment, the Enterprise Service Bus Apache ServiceMix 4.3 is used

System support for proactive adaptation

Applications in our modern, pervasive computing environments have to adapt themselves or their context in order to cope with changes. In the process, these pervasive applications should be as unobtrusive as possible, i.e., their adaptation should be automatic. In dynamic multi-user systems with shared resources and interactive applications, such adaptations cannot be scripted in advance. Instead, they have to be calculated at runtime. However, the necessary calculations quickly exceed the complexity that can be handled in real-time, i.e., without causing significant delays. The concept of proactive adaptation allows to change applications and/or context based on prediction of context and user behavior. Hence, proactive adaptation can reduce adaptation delays and avoid context interferences by determining coordinated adaptation plans ahead of time, instead of reactively when adaptation becomes necessary. Further, it helps to provide a seamless service to the user, while optimizing the overall system utility. This thesis presents a general framework and middleware-based system support for coordinated proactive adaptation in dynamic multi-user pervasive systems. The framework consists of five major components. The context interaction model and corresponding context broker offers context information, prediction, as well as actuation in a uniform fashion. The application configuration model allows applications to specify their requirements towards their context, as well as detail user preferences and duration-dependent utility and cost functions for adaptation optimization. Configuration algorithms calculate and rate all adaptation alternatives of an application given a current or predicted context and the specified rating functions, before coordination algorithms find interference-free adaptation plans for situations in which multiple applications share a context space. Finally, the adaptation control component combines the individual components of the framework in a two-dimensional control loop for proactive and fallback reactive adaptation. The prototype framework is evaluated in real-time simulations of an interactive pervasive system using recorded user traces

Flexible modeling and execution of choreographies

Approaches to address domain specific problems often share overlapping requirements but typically satisfy them in a unique manner for example using service-oriented concepts. The notion of Collaborative, Dynamic & Complex (CDC) systems has been proposed in literature to address the requirements of application domains such as eScience and Collective Adaptive Systems in a unified, generic manner. CDC systems are characterized by dealing with potentially large amounts of data and/or participating applications which engage in complex interactions specified by some collaboration protocol. Furthermore, the need for adaptation mechanisms is a common requirement and users from these application domains are typically no IT experts. The choreography concept originally known from collaborations in the business domain captures the interaction between independent parties from a global perspective. Each party is denoted as a choreography participant, which is implemented by a workflow or a service. This concept provides a way to model and execute for example complex eScience experiments involving multiple scientific fields, scientific methods, and time and/or length scales as a set of coupled workflows. However, typical choreography concepts as described in literature do not provide the desired level of flexibility and ease of use in both modeling and execution to address the requirements of users in CDC system application domains such as eScience. Thus, existing choreography concepts have to be considerably extended by introducing the Model-as-you-go for Choreographies approach in this thesis as a central notion providing capabilities for the flexible modeling and execution of choreographies. In the context of this approach, we provide a concept for fostering reuse in choreography modeling in the form of so-called choreography fragments. Such fragments can be extracted from existing and inserted into new choreography models in order to save time as well as reuse established and approved logic by inexperienced modelers in a less error-prone manner. Furthermore, we provide support for the user-driven control of the complete choreography life cycle. This effectively allows users to automatically deploy the workflow models implementing a choreography as well as starting, pausing, resuming, and terminating a choreography instance, which is formed through the collective execution of workflow instances. Most importantly, the underlying complexity of managing a set of coupled workflow instances is completely hidden from the users. Additional flexibility is given by a concept that allows to re-run already executed choreography logic in order to enforce the convergence of a calculation towards a particular result or to react to errors with parameter changes. The proposed concepts are implemented in a message-based system, the ChorSystem, which is able to handle the challenges of choreography life cycle management from deployment, to run time control and the re-run of logic. Furthermore, the modeling and run time monitoring are integrated into one graphical tool supporting the seamless transition from modeling to execution of choreographies. The concepts, their supporting algorithms, and the prototypical ChorSystem are validated by a set of case studies from different CDC system application domains and evaluated by performance measurements showing the practical applicability