Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward

Summary: Survey of ML for cloud data systems, outlining progress, practical deployments, and the gap between research promise and industry reality. Part II covers enterprise concerns: explanations, debugging, deployment, model management, data usage constraints, anonymization, and arising technical debt. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12529
Venue: VLDB
Year: 2021
Pagerank: 4.6872456e-05
Overall Rank: 7,655 | 46.75%
DOI: 10.14778/3476311.3476408

Incoming Non-self Citations Over Time

Authors

1. Alekh Jindal
2. Matteo Interlandi

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,690	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	5.997226e-05
4,934	From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management	2022	VLDB	5.8198826e-05
11,409	Machine Programming: Turning Data into Programmer Productivity	2022	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
102	The Case for Learned Index Structures	2018	SIGMOD	0.00049545203
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
1,019	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014625603
1,084	Dhalion: Self-Regulating Stream Processing in Heron	2017	VLDB	0.00014209714
1,254	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00013027411
1,703	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00010836769
1,855	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00010315245
2,083	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	9.5834572e-05
2,783	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	8.1293383e-05
3,625	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	6.9055212e-05
3,789	DIAMetrics: Benchmarking Query Engines at Scale	2020	VLDB	6.7644737e-05
3,806	HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning	2021	SIGMOD	6.7492837e-05
4,174	Computation Reuse in Analytics Job Service at Microsoft	2018	SIGMOD	6.3856219e-05
4,377	Understanding and Benchmarking the Impact of GDPR on Database Systems	2020	VLDB	6.2404627e-05
6,040	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD	5.2412035e-05
6,757	KEA: Tuning an Exabyte-Scale Data Infrastructure	2021	SIGMOD	4.9372134e-05
7,047	Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation	2021	VLDB	4.8521181e-05
7,684	AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft	2020	VLDB	4.6796855e-05
8,217	Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines	2020	SIGMOD	4.5568298e-05
8,220	PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!	2021	VLDB	4.5557328e-05
9,194	Phoebe: A Learning-based Checkpoint Optimizer	2021	VLDB	4.3761777e-05
9,735	SparkCruise: Handsfree Computation Reuse in Spark	2019	VLDB	4.2942813e-05

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3,875	Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML	2020	CIDR	6.675257e-05
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13,348	Machine Learning in the Real World	2016	VLDB	-
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