Data Pallets: Containerizing Storage For Reproducibility and Traceability.

Journal Article · 03 December 2019 · Lecture Notes in Computer Science

DOI: https://doi.org/10.1007/978-3-030-34356-9_4 · OSTI ID:1595037

Lofstead, Gerald Fredrick ^[1]; Baker, Joshua ^[2]; Younge, Andrew J ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Georgia Inst. of Technology, Atlanta, GA (United States)

Trusting simulation output is crucial for Sandia’s mission objectives. Here, we rely on these simulations to perform our high-consequence mission tasks given national treaty obligations. Other science and modeling applications, while they may have high-consequence results, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed workflow and provenance systems to aid in both automating simulation and modeling execution as well as determining exactly how was some output was created so that conclusions can be drawn from the data. Current approaches for workflows and provenance systems are all at the user level and have little to no system level support making them fragile, difficult to use, and incomplete solutions. The introduction of container technology is a first step towards encapsulating and tracking artifacts used in creating data and resulting insights, but their current implementation is focused solely on making it easy to deploy an application in an isolated “sandbox” and maintaining a strictly read-only mode to avoid any potential changes to the application. All storage activities are still using the system-level shared storage. This project explores extending the container concept to include storage as a new container type we call data pallets. Data Pallets are potentially writeable, auto generated by the system based on IO activities, and usable as a way to link the contained data back to the application and input deck used to create it.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Security
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1595037
• Report Number(s): SAND-2018-12861J; 669886
• Journal Information: Lecture Notes in Computer Science, Vol. 11887; ISSN 0302-9743
• Publisher: SpringerCopyright Statement
• Country of Publication: United States
• Language: English