5 research outputs found

    Towards Advanced Monitoring for Scientific Workflows

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    Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the components' and tasks' performance metrics, traces, and behavior are necessary to support the end user with a level of abstraction since the large amount of data cannot be analyzed manually. The execution and monitoring of scientific workflows involves many components, the cluster infrastructure, its resource manager, the workflow, and the workflow tasks. All components in such an execution environment access different monitoring metrics and provide metrics on different abstraction levels. The combination and analysis of observed metrics from different components and their interdependencies are still widely unregarded. We specify four different monitoring layers that can serve as an architectural blueprint for the monitoring responsibilities and the interactions of components in the scientific workflow execution context. We describe the different monitoring metrics subject to the four layers and how the layers interact. Finally, we examine five state-of-the-art scientific workflow management systems (SWMS) in order to assess which steps are needed to enable our four-layer-based approach.Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop SCDM 202

    Managing the risk of low falling numbers in wheat

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    Grain is purchased at a discount when falling numbers are below 300 seconds (sec). This can result in serious financial losses for farmers. This article addresses many commonly asked questions about the Hagberg-Perten Falling Number test, and provides some suggestions for reducing losses due to low falling numbers
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