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Towards Building Autonomous Data Services on Azure

Summary: ML-driven automation for Azure cloud data services to configure, optimize, and operate autonomous data services. Leverages workload traces and telemetry to meet SLAs and minimize cost, offering perspectives for providers and users on building autonomous, data-driven services. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6562
Venue
SIGMOD
Year
2023
Pagerank
5.4696038e-05
Overall Rank
8,193 | 43.79%
DOI
10.1145/3555041.3589674

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhu_sigmod23,
        title = {{Towards Building Autonomous Data Services on Azure}},
        author = {Zhu, Yiwen and Tian, Yuanyuan and Cahoon, Joyce and Krishnan, Subru and Agarwal, Ankita and Alotaibi, Rana and Camacho-Rodríguez, Jesús and Chundatt, Bibin and Chung, Andrew and Dutta, Niharika and Fogarty, Andrew and Gruenheid, Anja and Haynes, Brandon and Interlandi, Matteo and Iyer, Minu and Jurgens, Nick and Khushalani, Sumeet and Kroth, Brian and Kumar, Manoj and Leeka, Jyoti and Matusevych, Sergiy and Mittal, Minni and Mueller, Andreas and Muthyala, Kartheek and Nagulapalli, Harsha and Park, Yoonjae and Patel, Hiren and Pavlenko, Anna and Poppe, Olga and Ravindran, Santhosh and Saur, Karla and Sen, Rathijit and Suh, Steve and Tarafdar, Arijit and Waghray, Kunal and Wang, Demin and Curino, Carlo and Ramakrishnan, Raghu},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3555041.3589674},
        url = {https://dl.acm.org/doi/10.1145/3555041.3589674},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 26 of 26 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
184 DB2 Design Advisor: Integrated Automatic Physical Database Design 2004 VLDB 0.00026256101
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
854 Index Selection in a Self-Adaptive Data Base Management System 1976 SIGMOD 0.00013581028
1,092 Automated Demand-driven Resource Scaling in Relational Database-as-a-Service 2016 SIGMOD 0.00012221946
1,224 Dhalion: Self-Regulating Stream Processing in Heron 2017 VLDB 0.00011596911
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
2,226 Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless 2022 VLDB 8.9174082e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
3,400 A Demonstration of the OtterTune Automatic Database Management System Tuning Service 2018 VLDB 7.4433294e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,605 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2640711e-05
3,614 Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML 2020 CIDR 7.2568185e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
5,059 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.3807509e-05
6,121 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.9688569e-05
6,177 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.9496208e-05
6,553 Containerized Execution of UDFs: An Experimental Evaluation 2022 VLDB 5.8417517e-05
6,781 Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation 2021 VLDB 5.7764885e-05
6,946 KEA: Tuning an Exabyte-Scale Data Infrastructure 2021 SIGMOD 5.7309848e-05
7,619 AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft 2020 VLDB 5.5810604e-05
8,175 SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft 2021 VLDB 5.4737932e-05
8,864 Pipemizer: An Optimizer for Analytics Data Pipelines 2022 VLDB 5.355022e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
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