research-article

Netco: Cache and I/O Management for Analytics over Disaggregated Stores

Authors:

Virajith Jalaparti,

Ashvin Agrawal,

Avrilia Floratou,

Srikanth Kandula,

Joseph Seffi Naor,

Sriram RaoAuthors Info & Claims

SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

Pages 186 - 198

https://doi.org/10.1145/3267809.3267827

Published: 11 October 2018 Publication History

Abstract

We consider a common setting where storage is disaggregated from the compute in data-parallel systems. Colocating caching tiers with the compute machines can reduce load on the interconnect but doing so leads to new resource management challenges. We design a system Netco, which prefetches data into the cache (based on workload predictability), and appropriately divides the cache space and network bandwidth between the prefetches and serving ongoing jobs. Netco makes various decisions (what content to cache, when to cache and how to apportion bandwidth) to support end-to-end optimization goals such as maximizing the number of jobs that meet their service-level objectives (e.g., deadlines). Our implementation of these ideas is available within the open-source Apache HDFS project. Experiments on a public cloud, with production-trace inspired workloads, show that Netco uses up to 5x less remote I/O compared to existing techniques and increases the number of jobs that meet their deadlines up to 80%.

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SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

October 2018

546 pages

ISBN:9781450360111

DOI:10.1145/3267809

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https://doi.org/10.1016/j.future.2024.03.005
Durner DLeis VNeumann T(2023)Exploiting Cloud Object Storage for High-Performance AnalyticsProceedings of the VLDB Endowment10.14778/3611479.361148616:11(2769-2782)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611486
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https://dl.acm.org/doi/10.1145/3552326.3567499
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