A Cloud Ecosystem for Data and Software Developed by SCOPED
Observational seismology favors distributed memory workflows and embarrassing parallelization, for which cloud computing has an optimal architecture. Cloud computing offers horizontal scalability for computing workflows suited for single-station analysis. The orchestration of software, data, and resources on the cloud requires some thinking about the design. We present the initial experimentation of the SCOPED project. The SCOPED software ecosystem uses a base container and software-specific containers built on top, forming a registry of containers deployable on cloud instances. The SCOPED data uses S3-like objects, using mSEED files for raw seismic data and tileDB (the new Earthscope Consortium Cloud store) for data products. We propose the first cloudstore for Distributed Acoustic Sensing data. We illustrate the workflows using canonical examples in observational seismology: ambient-noise seismology and machine-learning earthquake workflows.
Session: Geophysical Data Analysis in Cloud Computing Environments [Poster]
Type: Poster
Room: Ballroom
Date: 4/18/2023
Presentation Time: 08:00 AM (local time)
Presenting Author: Carl Tape
Student Presenter: No
Additional Authors
Marine Denolle Corresponding Author mdenolle@uw.edu University of Washington |
Yinzhi Wang iwang@tacc.utexas.edu Texas Advanced Computing Center |
Carl Tape Presenting Author ctape@alaska.edu University of Alaska Fairbanks |
Yiyu Ni niyiyu@uw.edu University of Washington |
Felix Waldhauser felixw@ldeo.columbia.edu Columbia University |
Ebru Bozdag bozdag@mines.edu Colorado School of Mines |
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A Cloud Ecosystem for Data and Software Developed by SCOPED
Category
Geophysical Data Analysis in Cloud Computing Environments
Description