Conference Paper

Using High-Level Petri Nets for Hierarchical Grid Workflows

University of Muenster, Germany
DOI: 10.1109/E-SCIENCE.2006.261097 Conference: e-Science and Grid Computing, 2006. e-Science '06. Second IEEE International Conference on
Source: IEEE Xplore


An increasingly popular application programming model for Grids is to deploy often-used functionalities as remote services on high-performance hosts, following the principles of a service-oriented architecture. Complex applications are created by using several services and specifying a workflow between them. We discuss how workflows of Grid applications can be described easily as High-Level Petri Nets (HLPN), in order to orchestrate and execute distributed applications on the Grid automatically. In order to simplify the handling of complex and large-scale workflows, we introduce hierarchical Grid workflows, making use of the Petri Net refinement paradigm that allows to represent sub-workflows by single graph elements. We show how a complex application, the Barnes-Hut algorithm for N-Body simulation can be expressed as a hierarchical HLPN, using our platform-independent, XML-based Grid Workflow Description Language (GWorkflowDL). We discuss how the GWorkflowDL can be adapted to current Grid technologies, in particular to Java/RMI and the recent WSRF framework.

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    • "Abstract place and abstract transition both appear in the abstracted Petri nets to maintain the duality between places and transitions in [11]. Alt et al. [9] utilized Petri nets refinement paradigm to simplify the handling of complex and large-scale workflows, where sub-workflows are presented by composite transitions. The Petri nets abstraction is implemented through abstract places, abstract transitions and abstract tokens in [10]. "
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    ABSTRACT: Workflows are formal specifications of processes. In real life, workflows are very hard to manually specify. Groups of experts are usually needed, and the results are influenced by human perceptions. To solve these problems, workflows can be automatically learned from samples of execution processes. This idea is called Workflow Mining. Workflow Mining is usually performed using Petri Nets, which is a good approach for specifying workflows thanks to their high expressiveness. However, learning Petri nets is a very hard task because of their high complexity. This paper presents a new workflow mining algorithm called PALIA. This algorithm is able to learn workflows from parallel activity-based samples of processes using a simpler representation model called the Timed Parallel Automaton (TPA). In addition, a preliminary experiment to evaluate this proposal is also presented.
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