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Computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Thus, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation and data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.

Robust high throughput computing requires effective monitoring and enforcement of a variety of resources including CPU cores, memory, disk, and network traffic. Without effective monitoring and enforcement, it is easy to overload machines, causing failures and slowdowns, or underutilize machines, which results in wasted opportunities. This paper explores how to describe, measure, and enforce resources used by computational tasks. We focus on tasks running in distributed execution systems, in which a task requests the resources it needs, and the execution system ensures the availability of such resources. This presents two non-trivial problems: how to measure the resources consumed by a task, and how to monitor and report resource exhaustion in a robust and timely manner. For both of these tasks, operating systems have a variety of mechanisms with different degrees of availability, accuracy, overhead, and intrusiveness. We describe various forms of monitoring and the available mechanisms in contemporary operating systems. We then present two specific monitoring tools that choose different tradeoffs in overhead and accuracy, and evaluate them on a selection of benchmarks.

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