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Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries

Summary: Introduces IconqSched, a non‑intrusive scheduler that uses Iconq, a black‑box predictor of system runtime for concurrently running queries under varying system states, to model query interactions without changing DB internals. Using those predictions in a greedy submit/timing policy yields substantial end‑to‑end runtime reductions versus prior non‑intrusive heuristics (up to ~16.5% avg, 33.6% tail on Postgres; ~14.4% avg, 22.9% tail on Redshift). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14037
Venue
VLDB
Year
2025
Pagerank
4.1905499e-05
Overall Rank
10,733 | 25.41%
DOI
10.14778/3749646.3749686

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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
179 Efficient and Extensible Algorithms for Multi Query Optimization 2000 SIGMOD 0.00037637319
203 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034868567
329 Neo: A Learned Query Optimizer 2019 VLDB 0.00027301488
634 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00018844568
704 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017785557
716 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00017623897
804 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001643674
876 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015660534
1,017 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014627121
1,638 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00011050093
1,817 iCBS: Incremental Cost-based Scheduling under Piecewise Linear SLAs 2011 VLDB 0.00010428319
1,846 Query Optimization in Heterogeneous DBMS 1992 VLDB 0.00010338936
2,090 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 9.5668285e-05
3,131 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 7.5054309e-05
3,167 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 7.4561078e-05
3,222 WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases 2016 VLDB 7.3531422e-05
3,580 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 6.9460425e-05
3,819 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 6.7267885e-05
3,992 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 6.5519369e-05
4,075 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 6.4699689e-05
4,413 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 6.1989918e-05
4,592 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 6.056004e-05
4,681 Cloud Analytics Benchmark 2023 VLDB 5.9953512e-05
5,373 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 5.5410059e-05
5,643 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 5.3949759e-05
5,682 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 5.3752251e-05
5,844 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 5.3060581e-05
6,579 Tigger: A Database Proxy That Bounces With User-Bypass 2023 VLDB 4.9986207e-05
7,378 Distribution-Based Query Scheduling 2013 VLDB 4.7428007e-05
7,809 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 4.6415559e-05
7,994 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 4.607322e-05
9,916 Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes 2023 VLDB 4.2520778e-05
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