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A Polystore Based Database Operating System (DBOS)

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Book cover Heterogeneous Data Management, Polystores, and Analytics for Healthcare (DMAH 2020, Poly 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12633))

Abstract

Current operating systems are complex systems that were designed before today’s computing environments. This makes it difficult for them to meet the scalability, heterogeneity, availability, and security challenges in current cloud and parallel computing environments. To address these problems, we propose a radically new OS design based on data-centric architecture: all operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. We discuss how a database OS (DBOS) can improve the programmability and performance of many of today’s most important applications and propose a plan for the development of a DBOS proof of concept.

DBOS committee members in alphabetical order. The DBOS Committee, dbos-project@googlegroups.com.

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Notes

  1. 1.

    In this paper, we will use Lambda as an exemplar of any resource allocation system that supports “pay only for what you use.”.

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Acknowledgments

This work was partially supported by National Science Foundation CCF-1533644 and United States Air Force Research Laboratory Cooperative Agreement Number FA8750-19-2-1000. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the United States Air Force. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. The authors would also like to thank Charles Leiserson, Peter Michaleas, Albert Reuther, Michael Jones, and the MIT Supercloud Team.

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, USA

    Michael Cafarella

  2. MIT, Cambridge, USA

    David DeWitt, Tim Kraska & Michael Stonebraker

  3. MIT Lincoln Laboratory, Lexington, USA

    Vijay Gadepally & Jeremy Kepner

  4. Stanford University, Stanford, USA

    Christos Kozyrakis

  5. Stanford and Databricks, Berkeley, USA

    Matei Zaharia

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Correspondence to Vijay Gadepally .

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Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Tim Kraska

  5. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  6. University of Washington, Seattle, WA, USA

    Gang Luo

  7. Georgia State University, Atlanta, GA, USA

    Jun Kong

  8. Lucerne Unviersity of Applied Sciences, Rotkreuz, Switzerland

    Alevtina Dubovitskaya

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Cafarella, M. et al. (2021). A Polystore Based Database Operating System (DBOS). In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2020 2020. Lecture Notes in Computer Science(), vol 12633. Springer, Cham. https://doi.org/10.1007/978-3-030-71055-2_1

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