Workflow Management Service based on an Event-driven Computational Cloud

• Main Author: Chen, Ziwei
• Format: Others
• Language: English
• Published: KTH, Skolan för informations- och kommunikationsteknik (ICT) 2013
• Subjects: Software Engineering; Programvaruteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141696

The Event-driven computing paradigm, also known as trigger computing, is widely used in computer technology. Computer systems, such as database systems, introduce trigger mechanism to reduce repetitive human intervention. With the growing complexity of industrial use case requirements, independent and isolated triggers cannot fulfil the demands any more. Fortunately, an independent trigger can be triggered by the result produced by other triggered actions, and that enables the modelling of complex use cases, where the chains or graphs that consist of triggers are called workflows. Therefore, workflow construction and manipulation become a must for implementing the complex use cases. As the developing platform of this study, VISION Cloud is a computational storage system that executes small programs called storles as independent computation units in the storage. Similar to the trigger mechanism in database systems, storlets are triggered by specific events and then execute computations. As a result, one storlet can also be triggered by the result produced by other storlets, and it is called connections between storlets. Due to the growing complexity of use case requirements, an urgent demand is to have starlet workflow management supported in VISION system. Furthermore, because of the existence of connections between storlets in VISION, problems as non-termination triggering and unexpected overwriting appear as side-effects. This study develops a management service that consists of an analysis engine and a multi-level visualization interface. The analysis engine checks the connections between storlets by utilizing the technology of automatic theorem proving and deterministic finite automaton. The involved storlets and their connections are displayed in graphs via the multi-level visualization interface. Furthermore, the aforementioned connection problems are detected with graph theory algorithms. Finally, experimental results with practical use case examples demonstrate the correctness and comprehensiveness of the service. Algorithm performance and possible optimization are also discussed. They lead the way for future work to create a portable framework of event-driven workflow management services.