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Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries

Summary: IconqSched is a non-intrusive, cross-engine scheduler whose black-box Iconq predictor models concurrent-query runtime across system states. Greedy admission and timing decisions reduce end-to-end runtime by up to 16.5% on average and 33.6% at the tail on PostgreSQL/Redshift. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hdae9be58421b4604
Venue
VLDB
Year
2025
Pagerank
5.0715586e-05
Overall Rank
10,148 | 31.78%
DOI
10.14778/3749466.3749686

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wu_vldb25,
        title = {{Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries}},
        author = {Wu, Ziniu and Markakis, Markos and Liu, Chunwei and Chen, Peter Baile and Narayanaswamy, Balakrishnan and Kraska, Tim and Madden, Samuel},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4185--4198},
        doi = {10.14778/3749466.3749686},
        url = {https://doi.org/10.14778/3749466.3749686},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,930 Seron: Smart Query Router for Multi-Primary Cloud-Native Databases with Shared Storage 2026 VLDB 4.9793485e-05
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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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
129 Efficient and Extensible Algorithms for Multi Query Optimization 2000 SIGMOD 0.0003040756
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
460 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017842695
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
629 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015429949
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.0001481781
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,623 Query Optimization in Heterogeneous DBMS 1992 VLDB 0.00010045919
1,839 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 9.5304799e-05
2,106 iCBS: Incremental Cost-based Scheduling under Piecewise Linear SLAs 2011 VLDB 9.0316322e-05
2,250 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7533306e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
2,846 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.9453616e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
2,981 WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases 2016 VLDB 7.7851845e-05
3,316 Cloud Analytics Benchmark 2023 VLDB 7.4373161e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4207879e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2389387e-05
3,563 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.2042148e-05
4,666 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.479878e-05
5,041 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 6.3006152e-05
5,058 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2926774e-05
5,214 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.2248104e-05
5,545 Tigger: A Database Proxy That Bounces With User-Bypass 2023 VLDB 6.0886041e-05
6,872 Distribution-Based Query Scheduling 2013 VLDB 5.6592113e-05
7,157 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 5.5972283e-05
7,864 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 5.4367919e-05
9,115 Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes 2023 VLDB 5.2271833e-05
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