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WiSeDB: a learning-based workload management advisor for cloud databases

Authors:

Olga PapaemmanouilAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 9, Issue 10

Pages 780 - 791

https://doi.org/10.14778/2977797.2977804

Published: 01 June 2016 Publication History

Abstract

Workload management for cloud databases deals with the tasks of resource provisioning, query placement, and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation while aiming to optimize a single performance metric. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance goals and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present to the application alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance metrics and workload characteristics.

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Cited By

Lyu CFan QGuyard PDiao Y(2024)A Spark Optimizer for Adaptive, Fine-Grained Parameter TuningProceedings of the VLDB Endowment10.14778/3681954.368202117:11(3565-3579)Online publication date: 30-Aug-2024
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Wu ZMarcus RLiu ZNegi PNathan VPfeil PSaxena GRahman MNarayanaswamy BKraska TBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Stage: Query Execution Time Prediction in Amazon RedshiftCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653391(280-294)Online publication date: 9-Jun-2024
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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 9, Issue 10

June 2016

132 pages

ISSN:2150-8097

Editors:
Surajit Chaudhuri
Microsoft Research
,
Jayant Haritsa
I.I.Sc. Bangalore

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 June 2016

Published in PVLDB Volume 9, Issue 10

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Cited By

Lyu CFan QGuyard PDiao Y(2024)A Spark Optimizer for Adaptive, Fine-Grained Parameter TuningProceedings of the VLDB Endowment10.14778/3681954.368202117:11(3565-3579)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682021
Nathan VSingh VLiu ZRahman MKipf AHorn DPagano DSaxena GNarayanaswamy BKraska TBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Intelligent Scaling in Amazon RedshiftCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653394(269-279)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653394
Wu ZMarcus RLiu ZNegi PNathan VPfeil PSaxena GRahman MNarayanaswamy BKraska TBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Stage: Query Execution Time Prediction in Amazon RedshiftCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653391(280-294)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653391
Li WZhang JYin YLi YZhu ZZhou WLin LLi FBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDBCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653381(255-268)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653381
Wang JLi TWang ALiu XChen LChen JLiu JWu JLi FGao Y(2023)Real-Time Workload Pattern Analysis for Large-Scale Cloud DatabasesProceedings of the VLDB Endowment10.14778/3611540.361155716:12(3689-3701)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611557
Saxena GRahman MChainani NLin CCaragea GChowdhury FMarcus RKraska TPandis INarayanaswamy BDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Auto-WLM: Machine Learning Enhanced Workload Management in Amazon RedshiftCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589677(225-237)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589677
FUKUCHI KYAMADA H(2022)Leveraging Scale-Up Machines for Swift DBMS Replication on IaaS Platforms Using BalenaDBIEICE Transactions on Information and Systems10.1587/transinf.2020ZDP7505E105.D:1(92-104)Online publication date: 1-Jan-2022
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Lyu CFan QSong FSinha ADiao YChen WMa LFeng YLi YZeng KZhou J(2022)Fine-grained modeling and optimization for intelligent resource management in big data processingProceedings of the VLDB Endowment10.14778/3551793.355185515:11(3098-3111)Online publication date: 1-Jul-2022
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Liu XYin ZZhao CGe CChen LGao YLi DWang ZLiang GTan JLi F(2022)PinSQL: Pinpoint Root Cause SQLs to Resolve Performance Issues in Cloud Databases2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00236(2549-2561)Online publication date: May-2022
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Moeller JYe ZLin KLang WLi GLi ZIdreos SSrivastava D(2021)Toto Benchmarking the Efficiency of a Cloud ServiceProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457555(2543-2556)Online publication date: 9-Jun-2021
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