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E-LQO: A Comprehensive Energy Evaluation Framework for Learned Query Optimization

Summary: E-LQO evaluates energy across the full learned-query-optimization lifecycle, introducing EROI and energy payback time alongside latency. Across seven methods, supervision strategy—not learned vs. non-learned design—drives energy debt; execution-based methods peak near 50% template coverage. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID
hdfe0bc2ac02c8e66
Venue
SIGMOD
Year
2027
Pagerank
4.9605343e-05
Overall Rank
10,392 | 30.35%
DOI
10.1145/3837107
PDF
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Authors

BibTeX Citation

@inproceedings{liang_sigmod27,
        title = {{E-LQO: A Comprehensive Energy Evaluation Framework for Learned Query Optimization}},
        author = {Liang, Zibo and Xu, Quanqing and Chen, Xu and Chen, Junming and Xia, Yuyang and Zheng, Kai},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837107},
        url = {https://dl.acm.org/doi/10.1145/3837107},
        year = {2027}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 40 of 40 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0023948467
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
34 The Design Of Postgres 1986 SIGMOD 0.00049063803
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
254 The History of Histograms (abridged) 2003 VLDB 0.00022999992
355 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020146245
381 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019497061
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
458 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017870238
473 The Making of TPC-DS 2006 VLDB 0.00017656775
508 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017108973
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
887 Analyzing the Energy Efficiency of a Database Server 2010 SIGMOD 0.00013214226
1,058 Automated Demand-driven Resource Scaling in Relational Database-as-a-Service 2016 SIGMOD 0.00012225863
1,175 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011630532
1,293 Analysis of Two Existing and One New Dynamic Programming Algorithm for the Generation of Optimal Bushy Join Trees without Cross Products 2006 VLDB 0.00011143846
2,169 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.9151935e-05
2,190 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8848923e-05
2,619 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.1985665e-05
2,674 Energy Efficiency: The New Holy Grail of Data Management Systems Research 2009 CIDR 8.1274189e-05
2,768 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0199086e-05
2,805 Towards Eco-friendly Database Management Systems 2009 CIDR 7.9669837e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
4,096 Self-Tuning Query Scheduling for Analytical Workloads 2021 SIGMOD 6.7949494e-05
4,247 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6920059e-05
4,343 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.6397106e-05
4,459 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.5757572e-05
4,641 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.4739208e-05
4,833 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3834856e-05
5,320 PET: Reducing Database Energy Cost via Query Optimization 2012 VLDB 6.1668126e-05
5,457 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1058928e-05
5,876 Towards Energy-Efficient Database Cluster Design 2012 VLDB 5.945182e-05
6,109 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8676956e-05
9,011 Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries 2025 VLDB 5.2238303e-05
9,493 Workload-Aware CPU Performance Scaling for Transactional Database Systems 2018 SIGMOD 5.1546712e-05
9,642 Adaptive Energy-Control for In-Memory Database Systems 2018 SIGMOD 5.1338768e-05
9,756 APQO: An Adaptive Framework for Parametric Query Optimization 2026 SIGMOD 5.115551e-05
10,329 Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDB 2024 SIGMOD 5.0241309e-05
11,511 RankPQO: Learning-to-Rank for Parametric Query Optimization 2025 VLDB 4.9605343e-05
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