research-article

GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization

Authors:

Jianguo WangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 17, Issue 8

Pages 1939 - 1952

https://doi.org/10.14778/3659437.3659449

Published: 01 April 2024 Publication History

Abstract

Modern database management systems (DBMS) expose hundreds of configurable knobs to control system behaviours. Determining the appropriate values for these knobs to improve DBMS performance is a long-standing problem in the database community. As there is an increasing number of knobs to tune and each knob could be in continuous or categorical values, manual tuning becomes impractical. Recently, automatic tuning systems using machine learning methods have shown great potentials. However, existing approaches still incur significant tuning costs or only yield sub-optimal performance. This is because they either ignore the extensive domain knowledge available (e.g., DBMS manuals and forum discussions) and only rely on the runtime feedback of benchmark evaluations to guide the optimization, or they utilize the domain knowledge in a limited way. Hence, we propose GPTuner, a manual-reading database tuning system that leverages domain knowledge extensively and automatically to optimize search space and enhance the runtime feedback-based optimization process. Firstly, we develop a Large Language Model (LLM)-based pipeline to collect and refine heterogeneous knowledge, and propose a prompt ensemble algorithm to unify a structured view of the refined knowledge. Secondly, using the structured knowledge, we (1) design a workload-aware and training-free knob selection strategy, (2) develop a search space optimization technique considering the value range of each knob, and (3) propose a Coarse-to-Fine Bayesian Optimization Framework to explore the optimized space. Finally, we evaluate GPTuner under different benchmarks (TPC-C and TPC-H), metrics (throughput and latency) as well as DBMS (PostgreSQL and MySQL). Compared to the state-of-the-art approaches, GPTuner identifies better configurations in 16x less time on average. Moreover, GPTuner achieves up to 30% performance improvement (higher throughput or lower latency) over the best-performing alternative.

References

[1]

2024. GPTuner: full version. https://github.com/SolidLao/GPTuner/blob/main/gptuner-technical-report.pdf

[2]

Toufique Ahmed and Premkumar Devanbu. 2023. Few-shot training LLMs for project-specific code-summarization. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE '22). Association for Computing Machinery, New York, NY, USA, Article 177, 5 pages.

Digital Library

[3]

Sihem Amer-Yahia, Angela Bonifati, Lei Chen, Guoliang Li, Kyuseok Shim, Jianliang Xu, and Xiaochun Yang. 2023. From Large Language Models to Databases and Back: A discussion on research and education. arXiv:2306.01388 [cs.DB]

[4]

Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, and Saman Amarasinghe. 2014. OpenTuner: An Extensible Framework for Program Autotuning. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation (Edmonton, AB, Canada) (PACT '14). Association for Computing Machinery, New York, NY, USA, 303--316.

Digital Library

[5]

Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré. 2022. Ask Me Anything: A simple strategy for prompting language models. arXiv:2210.02441 [cs.CL]

[6]

Baoqing Cai, Yu Liu, Ce Zhang, Guangyu Zhang, Ke Zhou, Li Liu, Chunhua Li, Bin Cheng, Jie Yang, and Jiashu Xing. 2022. HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 646--659.

Digital Library

[7]

Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering 38, 4 (2015).

[8]

Stefano Cereda, Stefano Valladares, Paolo Cremonesi, and Stefano Doni. 2021. CGPTuner: A Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations under Varying Workload Conditions. Proc. VLDB Endow. 14, 8 (apr 2021), 1401--1413.

Digital Library

[9]

Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023. Binding Language Models in Symbolic Languages. arXiv:2210.02875 [cs.CL]

[10]

Benoît Dageville and Mohamed Zait. 2002. SQL Memory Management in Oracle9i. In Proceedings of the 28th International Conference on Very Large Data Bases (Hong Kong, China) (VLDB '02). VLDB Endowment, 962--973.

Digital Library

[11]

Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2023. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers. In Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, Toronto, Canada, 4005--4019.

[12]

Xiang Deng, Prashant Shiralkar, Colin Lockard, Binxuan Huang, and Huan Sun. 2022. Dom-lm: Learning generalizable representations for html documents. arXiv preprint arXiv:2201.10608 (2022).

[13]

Djellel Eddine Difallah, Andrew Pavlo, Carlo Curino, and Philippe Cudré-Mauroux. 2013. OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases. PVLDB 7, 4 (2013), 277--288. http://www.vldb.org/pvldb/vol7/p277-difallah.pdf

Digital Library

[14]

Songyun Duan, Vamsidhar Thummala, and Shivnath Babu. 2009. Tuning Database Configuration Parameters with ITuned. Proc. VLDB Endow. 2, 1 (aug 2009), 1246--1257.

Digital Library

[15]

EDB. 2023. https://www.enterprisedb.com/blog/tuning-maxwalsize-postgresql

[16]

Matthias Feurer and Frank Hutter. 2019. Hyperparameter optimization. Automated machine learning: Methods, systems, challenges (2019), 3--33.

[17]

HackerNews. 2023. https://news.ycombinator.com/item?id=28869509

[18]

Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. 2018. Deep reinforcement learning that matters. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.

[19]

Shrainik Jain, Bill Howe, Jiaqi Yan, and Thierry Cruanes. 2018. Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics. arXiv:1801.05613 [cs.DB]

[20]

Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022. OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering. arXiv:2207.03637 [cs.CL]

[21]

Konstantinos Kanellis, Ramnatthan Alagappan, and Shivaram Venkataraman. 2020. Too Many Knobs to Tune? Towards Faster Database Tuning by PreSelecting Important Knobs. In Proceedings of the 12th USENIX Conference on Hot Topics in Storage and File Systems (HotStorage'20). USENIX Association, USA, Article 16, 1 pages.

Digital Library

[22]

Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, and Shivaram Venkataraman. 2022. LlamaTune: Sample-Efficient DBMS Configuration Tuning. Proc. VLDB Endow. 15, 11 (jul 2022), 2953--2965.

Digital Library

[23]

Mayuresh Kunjir and Shivnath Babu. 2020. Black or White? How to Develop an AutoTuner for Memory-Based Analytics. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (Portland, OR, USA) (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 1667--1683.

Digital Library

[24]

Eva Kwan. 2002. Automatic Configuration for IBM ® DB 2 Universal Database TM Compressing years of performance tuning experience into seconds of execution. https://api.semanticscholar.org/CorpusID:15267980

[25]

Jiale Lao, Yibo Wang, Yufei Li, Jianping Wang, Yunjia Zhang, Zhiyuan Cheng, Wanghu Chen, Yuanchun Zhou, Mingjie Tang, and Jianguo Wang. 2024. A Demonstration of GPTuner: A GPT-Based Manual-Reading Database Tuning System. In Companion of the 2024 International Conference on Management of Data (Santiago, Chile) (SIGMOD '24). Association for Computing Machinery, New York, NY, USA, 4.

Digital Library

[26]

Guoliang Li, Xuanhe Zhou, Shifu Li, and Bo Gao. 2019. QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning. Proc. VLDB Endow. 12, 12 (aug 2019), 2118--2130.

Digital Library

[27]

Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2022. On the advance of making language models better reasoners. arXiv preprint arXiv:2206.02336 (2022).

[28]

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter. 2022. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research 23, 54 (2022), 1--9. http://jmlr.org/papers/v23/21-0888.html

[29]

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter. 2022. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research 23, 54 (2022), 1--9. http://jmlr.org/papers/v23/21-0888.html

[30]

Colin Lockard, Prashant Shiralkar, and Xin Luna Dong. 2019. Openceres: When open information extraction meets the semi-structured web. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 3047--3056.

[31]

Colin Lockard, Prashant Shiralkar, Xin Luna Dong, and Hannaneh Hajishirzi. 2020. Zeroshotceres: Zero-shot relation extraction from semi-structured web-pages. arXiv preprint arXiv:2005.07105 (2020).

[32]

Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023. ChatGPT as a Factual Inconsistency Evaluator for Text Summarization. arXiv:2303.15621 [cs.CL]

[33]

Michael D. McKay. 1992. Latin Hypercube Sampling as a Tool in Uncertainty Analysis of Computer Models. In Proceedings of the 24th Conference on Winter Simulation (Arlington, Virginia, USA) (WSC '92). Association for Computing Machinery, New York, NY, USA, 557--564.

Digital Library

[34]

Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, and Christopher Ré. 2022. Can Foundation Models Wrangle Your Data? arXiv:2205.09911 [cs.LG]

[35]

Fatemeh Nargesian, Erkang Zhu, Renée J Miller, Ken Q Pu, and Patricia C Arocena. 2019. Data lake management: challenges and opportunities. Proceedings of the VLDB Endowment 12, 12 (2019), 1986--1989.

Digital Library

[36]

OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]

[37]

Andrew Pavlo, Gustavo Angulo, Joy Arulraj, Haibin Lin, Jiexi Lin, Lin Ma, Prashanth Menon, Todd C Mowry, Matthew Perron, Ian Quah, et al. 2017. Self-Driving Database Management Systems. In CIDR, Vol. 4. 1.

[38]

Andrew Pavlo, Matthew Butrovich, Lin Ma, Prashanth Menon, Wan Shen Lim, Dana Van Aken, and William Zhang. 2021. Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation. Proc. VLDB Endow. 14, 12 (jul 2021), 3211--3221.

Digital Library

[39]

PostgreSQL. 2023. https://www.postgresql.org/docs/current/runtime-config-resource.html

[40]

PostgreSQL. 2023. https://www.postgresql.org/docs/current/index.html

[41]

PostgresqlCO.NF. 2023. https://postgresqlco.nf/

[42]

Omer Sagi and Lior Rokach. 2018. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, 4 (2018), e1249.

[43]

Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021. PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. arXiv:2109.05093 [cs.CL]

[44]

Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, and Christopher Ré. 2015. Incremental knowledge base construction using deepdive. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol. 8. NIH Public Access, 1310.

Digital Library

[45]

Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25 (2012).

[46]

David G. Sullivan, Margo I. Seltzer, and Avi Pfeffer. 2004. Using Probabilistic Reasoning to Automate Software Tuning. In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems (New York, NY, USA) (SIGMETRICS '04/Performance '04). Association for Computing Machinery, New York, NY, USA, 404--405.

Digital Library

[47]

Xiu Tang, Sai Wu, Mingli Song, Shanshan Ying, Feifei Li, and Gang Chen. 2022. PreQR: Pre-training Representation for SQL Understanding. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 204--216.

Digital Library

[48]

Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2013. Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Chicago, Illinois, USA) (KDD '13). Association for Computing Machinery, New York, NY, USA, 847--855.

Digital Library

[49]

Immanuel Trummer. 2021. Can deep neural networks predict data correlations from column names? arXiv preprint arXiv:2107.04553 (2021).

[50]

Immanuel Trummer. 2022. DB-BERT: A Database Tuning Tool That "Reads the Manual". In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 190--203.

Digital Library

[51]

Immanuel Trummer. 2023. Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4. Proceedings of the VLDB Endowment 16, 12 (2023), 4098--4101.

Digital Library

[52]

Immanuel Trummer, Junxiong Wang, Ziyun Wei, Deepak Maram, Samuel Moseley, Saehan Jo, Joseph Antonakakis, and Ankush Rayabhari. 2021. SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning. ACM Trans. Database Syst. 46, 3, Article 9 (sep 2021), 45 pages.

Digital Library

[53]

Dana Van Aken, Andrew Pavlo, Geoffrey J. Gordon, and Bohan Zhang. 2017. Automatic Database Management System Tuning Through Large-Scale Machine Learning. In Proceedings of the 2017 ACM International Conference on Management of Data (Chicago, Illinois, USA) (SIGMOD '17). Association for Computing Machinery, New York, NY, USA, 1009--1024.

Digital Library

[54]

Dana Van Aken, Dongsheng Yang, Sebastien Brillard, Ari Fiorino, Bohan Zhang, Christian Bilien, and Andrew Pavlo. 2021. An Inquiry into Machine Learning-Based Automatic Configuration Tuning Services on Real-World Database Management Systems. Proc. VLDB Endow. 14, 7 (mar 2021), 1241--1253.

Digital Library

[55]

Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, and Yue Zhang. 2023. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity. arXiv:2310.07521 [cs.CL]

[56]

Jiaqi Wang, Tianyi Li, Anni Wang, Xiaoze Liu, Lu Chen, Jie Chen, Jianye Liu, Junyang Wu, Feifei Li, and Yunjun Gao. 2023. Real-time Workload Pattern Analysis for Large-scale Cloud Databases. arXiv preprint arXiv:2307.02626 (2023).

[57]

Junxiong Wang, Immanuel Trummer, and Debabrota Basu. 2021. UDO: universal database optimization using reinforcement learning. Proceedings of the VLDB Endowment 14, 13 (2021), 3402--3414.

Digital Library

[58]

Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023. Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning. arXiv:2301.13808 [cs.CL]

[59]

Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2019. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. arXiv:1809.08887 [cs.CL]

[60]

Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, and Ion Stoica. 2016. Apache Spark: a unified engine for big data processing. Commun. ACM 59, 11 (oct 2016), 56--65.

Digital Library

[61]

Ji Zhang, Yu Liu, Ke Zhou, Guoliang Li, Zhili Xiao, Bin Cheng, Jiashu Xing, Yangtao Wang, Tianheng Cheng, Li Liu, Minwei Ran, and Zekang Li. 2019. An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD '19). Association for Computing Machinery, New York, NY, USA, 415--432.

Digital Library

[62]

Xinyi Zhang, Zhuo Chang, Yang Li, Hong Wu, Jian Tan, Feifei Li, and Bin Cui. 2022. Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation. Proc. VLDB Endow. 15, 9 (may 2022), 1808--1821.

Digital Library

[63]

Xinyi Zhang, Hong Wu, Zhuo Chang, Shuowei Jin, Jian Tan, Feifei Li, Tieying Zhang, and Bin Cui. 2021. ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases. In Proceedings of the 2021 International Conference on Management of Data (Virtual Event, China) (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 2102--2114.

Digital Library

[64]

Xinyi Zhang, Hong Wu, Yang Li, Jian Tan, Feifei Li, and Bin Cui. 2022. Towards Dynamic and Safe Configuration Tuning for Cloud Databases. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 631--645.

Digital Library

[65]

Yunjia Zhang, Avrilia Floratou, Joyce Cahoon, Subru Krishnan, Andreas C Müller, Dalitso Banda, Fotis Psallidas, and Jignesh M Patel. 2023. Schema Matching using Pre-Trained Language Models. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 1558--1571.

[66]

Yunjia Zhang, Jordan Henkel, Avrilia Floratou, Joyce Cahoon, Shaleen Deep, and Jignesh M. Patel. 2023. ReAcTable: Enhancing ReAct for Table Question Answering. arXiv:2310.00815 [cs.DB]

[67]

Xinyang Zhao, Xuanhe Zhou, and Guoliang Li. 2023. Automatic Database Knob Tuning: A Survey. IEEE Transactions on Knowledge and Data Engineering (2023), 1--20.

Digital Library

[68]

Xuanhe Zhou, Guoliang Li, and Zhiyuan Liu. 2023. LLM As DBA. arXiv:2308.05481 [cs.DB]

[69]

Yuqing Zhu, Jianxun Liu, Mengying Guo, Yungang Bao, Wenlong Ma, Zhuoyue Liu, Kunpeng Song, and Yingchun Yang. 2017. BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning. In Proceedings of the 2017 Symposium on Cloud Computing (Santa Clara, California) (SoCC '17). Association for Computing Machinery, New York, NY, USA, 338--350.

Digital Library

Cited By

Wang QLan TTang YSang BHuang ZDu YZhang HSha JLu HZhou YZhang KTang M(2024)DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the CloudProceedings of the VLDB Endowment10.14778/3685800.368583217:12(4130-4144)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685832
Zhao TFaruqui N(2024)EconoFormer: A Novel Macroeconomic Policy Analysis and Implementation Planner using Generative Transformer ModelIEEE Access10.1109/ACCESS.2024.3512594(1-1)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3512594
Fan CPan ZSun WYang CChen W(2024)LATuner: An LLM-Enhanced Database Tuning System Based on Adaptive Surrogate ModelMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70362-1_22(372-388)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70362-1_22

Recommendations

DB-BERT: A Database Tuning Tool that "Reads the Manual"
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT ...
A Demonstration of GPTuner: A GPT-Based Manual-Reading Database Tuning System
SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of Data

Selecting appropriate values for the configurable knobs of Database Management Systems (DBMS) is crucial to improve performance. But because such complexity has surpassed the abilities of even the best human experts, database community turns to machine ...
Automatic Database Management System Tuning Through Large-scale Machine Learning
SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data

Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 17, Issue 8

April 2024

335 pages

Editors:
Meihui Zhang
Beijing Institute of Technology
,
Cyrus Shahabi
University of Southern California

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 April 2024

Published in PVLDB Volume 17, Issue 8

Check for updates

Badges

Artifacts Available / v1.1

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
289
Total Downloads

Downloads (Last 12 months)289
Downloads (Last 6 weeks)11

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang QLan TTang YSang BHuang ZDu YZhang HSha JLu HZhou YZhang KTang M(2024)DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the CloudProceedings of the VLDB Endowment10.14778/3685800.368583217:12(4130-4144)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685832
Zhao TFaruqui N(2024)EconoFormer: A Novel Macroeconomic Policy Analysis and Implementation Planner using Generative Transformer ModelIEEE Access10.1109/ACCESS.2024.3512594(1-1)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3512594
Fan CPan ZSun WYang CChen W(2024)LATuner: An LLM-Enhanced Database Tuning System Based on Adaptive Surrogate ModelMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70362-1_22(372-388)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70362-1_22

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents