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Self-tuning Eventually-Consistent Data Stores

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Stabilization, Safety, and Security of Distributed Systems (SSS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10616))

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Abstract

Replication protocols in distributed storage systems are fundamentally constrained by the finite propagation speed of information, which necessitates trade-offs among performance metrics even in the absence of failures. We focus on the consistency-latency trade-off, which dictates that a distributed storage system can either guarantee that clients always see the latest data, or it can guarantee that operation latencies are small (relative to the inter-data-center latencies) but not both. We propose a technique called spectral shifting for tuning this trade-off adaptively to meet an application-specific performance target in a dynamically changing environment. Experiments conducted in a real wold cloud computing environment demonstrate that our tuning framework provides superior convergence compared to a state-of-the-art solution.

W. Golab—Author supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada, Discovery Grants Program, by a Google Faculty Research Award, and by the AWS Cloud Credits for Research program.

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Authors and Affiliations

  1. University of Waterloo, Waterloo, Canada

    Shankha Chatterjee & Wojciech Golab

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  1. Shankha Chatterjee
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  2. Wojciech Golab
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Correspondence to Shankha Chatterjee .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, United Kingdom

    Paul Spirakis

  2. Chalmers University of Technology, Gothenburg, Sweden

    Philippas Tsigas

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Chatterjee, S., Golab, W. (2017). Self-tuning Eventually-Consistent Data Stores. In: Spirakis, P., Tsigas, P. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2017. Lecture Notes in Computer Science(), vol 10616. Springer, Cham. https://doi.org/10.1007/978-3-319-69084-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-69084-1_6

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