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

Summary: Auto-WLM is ML-driven WLM for Redshift that auto-tunes concurrency and memory to maximize throughput under workloads. Locally trained query performance models predict runtime and memory to guide millions of scheduling decisions in real time. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6502
Venue: SIGMOD
Year: 2023
Pagerank: 6.056004e-05
Overall Rank: 4,592 | 68.09%
DOI: 10.1145/3555041.3589677

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 23 of 23 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,131	Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet	2024	VLDB	7.5054309e-05
5,643	Intelligent Scaling in Amazon Redshift	2024	SIGMOD	5.3949759e-05
5,844	Stage: Query Execution Time Prediction in Amazon Redshift	2024	SIGMOD	5.3060581e-05
7,564	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	4.7049893e-05
7,994	Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD	2024	VLDB	4.607322e-05
8,223	Automated Multidimensional Data Layouts in Amazon Redshift	2024	SIGMOD	4.5509217e-05
8,834	ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation	2024	SIGMOD	4.4351469e-05
8,961	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	4.4171776e-05
9,215	PACE: Poisoning Attacks on Learned Cardinality Estimation	2024	SIGMOD	4.3679174e-05
9,233	LeaFi: Data Series Indexes on Steroids with Learned Filters	2025	SIGMOD	4.3648789e-05
9,645	PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking	2025	VLDB	4.3067693e-05
10,216	The Case For Language Model Approximated LIKE Predicate	2026	SIGMOD	4.1905499e-05
10,265	AQD: Online Adaptive Query Dispatcher for HTAP Databases	2026	VLDB	4.1905499e-05
10,322	SafeLoad: Efficient Admission Control Framework for Identifying Memory-Overloading Queries in Cloud Data Warehouses	2026	VLDB	4.1905499e-05
10,416	Flux: Unifying Heterogeneous Infrastructure for Alibaba AnalyticDB	2025	SIGMOD	4.1905499e-05
10,501	Intra-Query Runtime Elasticity for Cloud-Native Data Analysis	2025	SIGMOD	4.1905499e-05
10,573	PlanRGCN: Predicting SPARQL Query Performance	2025	VLDB	4.1905499e-05
10,733	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	4.1905499e-05
10,844	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.1905499e-05
10,856	CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics	2025	VLDB	4.1905499e-05
10,876	LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases	2025	VLDB	4.1905499e-05
10,935	Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDB	2024	SIGMOD	4.1905499e-05
10,990	DPconv: Super-Polynomially Faster Join Ordering	2024	SIGMOD	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 33 of 33 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059446482
101	The Case for Learned Index Structures	2018	SIGMOD	0.00049778866
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036859633
203	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034868567
208	Schism: a Workload-Driven Approach to Database Replication and Partitioning	2010	VLDB	0.00034478612
293	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028661817
329	Neo: A Learned Query Optimizer	2019	VLDB	0.00027301488
371	Self-Driving Database Management Systems	2017	CIDR	0.00025382677
423	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00023628474
634	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018844568
752	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00017138049
796	SageDB: A Learned Database System	2019	CIDR	0.00016541749
844	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00015964123
876	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00015660534
1,273	Amazon Redshift Re-invented	2022	SIGMOD	0.00012870386
1,464	Learning Multi-dimensional Indexes	2020	SIGMOD	0.0001184772
1,856	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00010319105
1,887	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00010201938
2,050	Automatically Indexing Millions of Databases in Microsoft Azure SQL Database	2019	SIGMOD	9.6883066e-05
2,090	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	9.5668285e-05
2,154	SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning	2018	VLDB	9.4176683e-05
2,309	On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems	2012	VLDB	9.0630462e-05
2,777	Quickstep: A Data Platform Based on the Scaling-Up Approach	2018	VLDB	8.1346418e-05
3,222	WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases	2016	VLDB	7.3531422e-05
3,819	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	6.7267885e-05
4,413	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	6.1989918e-05
4,547	Database-Agnostic Workload Management	2019	CIDR	6.0904384e-05
4,687	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	5.9915268e-05
4,966	Releasing Cloud Databases from the Chains of Performance Prediction Models	2017	CIDR	5.7949047e-05
5,210	Self-Tuning Query Scheduling for Analytical Workloads	2021	SIGMOD	5.6244961e-05
5,443	NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning	2018	SIGMOD	5.5020716e-05
5,682	LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems	2022	SIGMOD	5.3752251e-05
5,994	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD	5.2367998e-05

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