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LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems
Summary: LSched is a fully learned, workload-aware query scheduler for in-memory analytical DBs, enabling inter- and intra-query scheduling under dynamic workloads. It accounts for operator types and pipelining, beating heuristics and prior RL by 35-50% on TPC-H, SSB, and JOB.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6469
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.3803919e-05
- Overall Rank
- 5,671 | 60.55%
- DOI
-
10.1145/3514221.3526158
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 4,462 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1611784e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 5,314 |
Can Learned Models Replace Hash Functions? |
2023 |
VLDB |
5.5724608e-05 |
| 5,634 |
Intelligent Scaling in Amazon Redshift |
2024 |
SIGMOD |
5.4000904e-05 |
| 8,417 |
The Case for Learned In-Memory Joins |
2023 |
VLDB |
4.5194164e-05 |
| 9,345 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3536343e-05 |
| 9,827 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2751057e-05 |
| 10,190 |
P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,265 |
AQD: Online Adaptive Query Dispatcher for HTAP Databases |
2026 |
VLDB |
4.1945683e-05 |
| 10,491 |
Intra-Query Runtime Elasticity for Cloud-Native Data Analysis |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,726 |
Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries |
2025 |
VLDB |
4.1945683e-05 |
| 10,872 |
LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases |
2025 |
VLDB |
4.1945683e-05 |
| 10,932 |
Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDB |
2024 |
SIGMOD |
4.1945683e-05 |
| 11,197 |
QaaD (Query-as-a-Data): Scalable Execution of Massive Number of Small Queries in Spark |
2023 |
SIGMOD |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 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 |
0.00059038975 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 182 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036962631 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 418 |
Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age |
2014 |
SIGMOD |
0.00023729211 |
| 569 |
Optimizing Space Amplification in RocksDB |
2017 |
CIDR |
0.00019924098 |
| 596 |
HYRISE—A Main Memory Hybrid Storage Engine |
2011 |
VLDB |
0.00019481482 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 735 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00017452467 |
| 826 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016224841 |
| 857 |
The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds |
2020 |
VLDB |
0.00015882892 |
| 1,478 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.00011762542 |
| 1,611 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011147324 |
| 1,889 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010200865 |
| 2,115 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5257379e-05 |
| 2,678 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.3252088e-05 |
| 2,772 |
Quickstep: A Data Platform Based on the Scaling-Up Approach |
2018 |
VLDB |
8.1401661e-05 |
| 4,097 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4551616e-05 |
| 4,282 |
Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement |
2015 |
VLDB |
6.293052e-05 |
| 5,212 |
Self-Tuning Query Scheduling for Analytical Workloads |
2021 |
SIGMOD |
5.6262923e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 7,461 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
4.723898e-05 |
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