Standard client-server workflow management systems have an intrinsic scalability limitation, the central server, which represents a bottleneck for large-scale applications. This server is also a single failure point t...
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Standard client-server workflow management systems have an intrinsic scalability limitation, the central server, which represents a bottleneck for large-scale applications. This server is also a single failure point that may disable the whole system. We propose a fully distributed architecture for workflow management systems. It is based on the idea that the case (an instance of the process) migrates from host to host, following a process plan, while the case activities are executed. This basic architecture is improved so that other requirements for Work-flow Management Systems, besides scalability, are also contemplated. A CORBA-based implementation of such architecture is discussed, with its limitations, advantages and project decisions described.
The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners...
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The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task;and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by
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