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Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift

Authors:

Mohammad Rahman,

Naresh Chainani,

George Caragea,

Fahim Chowdhury,

Ippokratis Pandis,

Balakrishnan (Murali) NarayanaswamyAuthors Info & Claims

SIGMOD '23: Companion of the 2023 International Conference on Management of Data

Pages 225 - 237

https://doi.org/10.1145/3555041.3589677

Published: 05 June 2023 Publication History

Abstract

There has been a lot of excitement around using machine learning to improve the performance and usability of database systems. However, few of these techniques have actually been used in the critical path of customer-facing database services. In this paper, we describe Auto-WLM, a machine learning based automatic workload manager currently used in production in Amazon Redshift. Auto-WLM is an example of how machine learning can improve the performance of large data-warehouses in practice and at scale. Auto-WLM intelligently schedules workloads to maximize throughput and horizontally scales clusters in response to workload spikes. While traditional heuristic-based workload management requires a lot of manual tuning (e.g. of the concurrency level, memory allocated to queries etc.) for each specific workload, Auto-WLM does this tuning automatically and as a result is able to quickly adapt and react to workload changes and demand spikes. At its core, Auto-WLM uses locally-trained query performance models to predict the query execution time and memory needs for each query, and uses this to make intelligent scheduling decisions. Currently, Auto-WLM makes millions of decisions every day, and constantly optimizes the performance for each individual Amazon Redshift cluster. In this paper, we will describe the advantages and challenges of implementing and deploying Auto-WLM, as well as outline areas of research that may be of interest to those in the "ML for systems'' community with an eye for practicality.

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Cited By

Yu GWu ZKossmann FLi TMarkakis MNgom AMadden SKraska T(2024)Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRADProceedings of the VLDB Endowment10.14778/3681954.368202617:11(3629-3643)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682026
Milicevic BBabovic Z(2024)A systematic review of deep learning applications in database query executionJournal of Big Data10.1186/s40537-024-01025-111:1Online publication date: 18-Dec-2024
https://doi.org/10.1186/s40537-024-01025-1
Stoian MKipf A(2024)DPconv: Super-Polynomially Faster Join OrderingProceedings of the ACM on Management of Data10.1145/36988092:6(1-26)Online publication date: 20-Dec-2024
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Index Terms

Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs

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cover image ACM Conferences

SIGMOD '23: Companion of the 2023 International Conference on Management of Data

June 2023

330 pages

ISBN:9781450395076

DOI:10.1145/3555041

General Chairs:
Sudipto Das
Amazon Web Services, USA
,
Ippokratis Pandis
Amazon Web Services, USA
,
Program Chairs:
K. Selçuk Candan
Arizona State University, USA
,
Sihem Amer-Yahia
CNRS, Université Grenoble Alpes, France

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 05 June 2023

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https://dl.acm.org/doi/10.14778/3681954.3682026
Milicevic BBabovic Z(2024)A systematic review of deep learning applications in database query executionJournal of Big Data10.1186/s40537-024-01025-111:1Online publication date: 18-Dec-2024
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Stoian MKipf A(2024)DPconv: Super-Polynomially Faster Join OrderingProceedings of the ACM on Management of Data10.1145/36988092:6(1-26)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3698809
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