DBScholar

Back to papers

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking

Summary: PBench synthesizes cloud analytics workloads that replicate real execution statistics (performance metrics, operator distributions, temporal dynamics) by selecting and combining benchmark components and augmenting missing pieces. Key innovations: multi-objective optimization for component selection, progressive timestamp assignment, and LLM-based component augmentation to preserve statistical fidelity, achieving up to 6x lower approximation error vs prior work. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14196
Venue
VLDB
Year
2025
Pagerank
5.2209769e-05
Overall Rank
9,777 | 32.93%
DOI
10.14778/3749646.3749661

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhou_vldb25,
        title = {{PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking}},
        author = {Zhou, Yan and Liu, Chunwei and Urgaonkar, Bhuvan and Wang, Zhengle and Mueller, Magnus and Zhang, Chao and Zhang, Songyue and Pfeil, Pascal and Horn, Dominik and Liu, Zhengchun and Pagano, Davide and Kraska, Tim and Madden, Samuel and Fan, Ju},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {3883--3895},
        doi = {10.14778/3749646.3749661},
        url = {https://doi.org/10.14778/3749646.3749661},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,565 LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration 2026 VLDB 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

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

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
66 The Snowflake Elastic Data Warehouse 2016 SIGMOD 0.00038561587
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
818 Amazon Redshift Re-invented 2022 SIGMOD 0.00013822916
2,202 Quantifying TPC-H Choke Points and Their Optimizations 2020 VLDB 8.9639459e-05
2,344 Towards Cost-Optimal Query Processing in the Cloud 2021 VLDB 8.7199754e-05
2,408 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 8.6154404e-05
3,173 Why You Should Run TPC-DS:A Workload Analysis 2007 VLDB 7.6664516e-05
3,460 DIAMetrics: Benchmarking Query Engines at Scale 2020 VLDB 7.3939262e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,838 Cloud Analytics Benchmark 2023 VLDB 7.0823175e-05
3,903 Keep It Simple: Testing Databases via Differential Query Plans 2024 SIGMOD 7.0304586e-05
4,261 Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration 2020 VLDB 6.7982037e-05
5,107 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3623786e-05
5,169 LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning 2022 SIGMOD 6.3349035e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,369 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.2437078e-05
5,431 HyBench: A New Benchmark for HTAP Databases 2024 VLDB 6.2211765e-05
7,589 Cost-Intelligent Data Analytics in the Cloud 2024 CIDR 5.5907048e-05
7,901 Cloud Databases: New Techniques, Challenges, and Opportunities 2022 VLDB 5.5185913e-05
Previous Page 1 / 1 Next

Semantically Similar Papers