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Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. The workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.
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Poster: Robust Meta-Workflow Management with Mufasa
Workflow management systems (WMS) are widely used to describe and execute large computational or data intensive applications. However, when a large ensemble of workflows is run on a cluster, new resource management problems occur. Each WMS itself consumes otherwise unmanaged resources, such as the shared head node where the WMS coordinator runs, the shared filesystem where intermediate data is stored, and the shared batch queue itself. We introduce Mufasa, a meta-workflow management system, which is designed to control the concurrency of multiple workflows in an ensemble, by observing and controlling the resources required by each WMS. We show some initial results demonstrating that Mufasa correctly handles the overcommitment of different resource types by starting, pausing, and cancelling workflows with unexpected behavior.
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- Award ID(s):
- 1931348
- PAR ID:
- 10356918
- Date Published:
- Journal Name:
- IEEE International Conference on eScience
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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