research-article

Autonomic tuning expert: a framework for best-practice oriented autonomic database tuning

Authors:

Gennadi Rabinovitch,

Michael Reichert,

Stephan ArenswaldAuthors Info & Claims

CASCON '08: Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds

Article No.: 3, Pages 27 - 41

https://doi.org/10.1145/1463788.1463792

Published: 27 October 2008 Publication History

Abstract

Databases are growing rapidly in scale and complexity. High performance, availability, and further service level agreements need to be satisfied under any circumstances to please customers. In order to tune the DBMS within their complex environments, highly skilled database administrators (DBAs) are required. Unfortunately, they are becoming rarer and more and more expensive. Improving performance analysis and moving towards the automation of large information management platforms requires a more intuitive and flexible source of decision making.

This paper points out the importance of best-practices knowledge for autonomic database tuning and addresses the idea of formalizing and storing DBA expert tuning knowledge for the autonomic management process. We will focus our attention on the development of a reference system for best-practice oriented autonomic database tuning for IBM DB2 and subsequently evaluate our system's tuning performance under changing workload.

References

[1]

D. Benoit. Automatic Diagnosis of Performance Problems in Database Management Systems. In Proceedings of the Second International Conference on Autonomic Computing, Seattle, Washington, 2005.

Digital Library

[2]

A. Biazetti, K. Gajda. Achieving complex event processing with Active Correlation Technology. November 2005.

[3]

J. P. Bigus, J. L. Hellerstein, and M. S. Squillante. Auto Tune: A Generic Agent for Automated Performance Tuning. In Proceedings of the International Conference on Practical Application of Intelligent Agents and Multi-Agents, 2000.

[4]

K. P. Brown, M. Mehta, M. J. Carey, et al. Towards Automated Performance Tuning for Complex Workloads. In Proceedings of VLDB, 1994.

Digital Library

[5]

S. Chakravarthy, V. Krishnaprasad, E. Anwar, S. Kim. Composite Events for Active Databases: Semantics Contexts and Detection. In Proceedings of VLDB, 1994.

Digital Library

[6]

S. Chaudhuri, G. Weikum. Rethinking Database System Architecture: Towards a Self-tuning RISC style Database System. In Proceedings of VLDB, 2000.

Digital Library

[7]

W. J. Chen, U. Baumbach, M. Miskimen, et al. DB2 Performance Expert for Multiplatforms V2.2, March 2006.

[8]

S. Elnaffar. Towards Workload-Aware DBMSs: Identifying Workload Type and Predicting its Change. PhD Thesis. Queen's University, Canada, 2004.

Digital Library

[9]

S. Elnaffar, W. Powley, D. Benoit, et al. Today's DBMSs: How Autonomic Are They? First International Workshop on Autonomic Computing Systems, Prague, 2003.

[10]

Enterprise Management Associates: Practical Autonomic Computing - Roadmap to Self Managing Technology. White Paper. 2006.

[11]

P. Horn. Autonomic Computing: IBM's Perspective on the State of Information Technology. White Paper. IBM Research. 2001.

[12]

IBM Corp. Autonomic Computing Toolkit: Developer's Guide. Technical Report SC30-4083-02, IBM, August 2004.

[13]

IBM Corp. DB2 UDB ESE V8 non-DPF Performance Guide for High Performance OLTP and BI. IBM Redbooks. 2004.

Digital Library

[14]

IBM Corp. An Architectural Blueprint for Autonomic Computing. White Paper. IBM Research Lab, 2004.

[15]

IBM Corp. DB2 Universal Database - Administration Guide: Performance (Version 8). 2002.

[16]

IBM Corp. DB2 Universal Database - System Monitor Guide and Reference (Version 8). 2004.

[17]

G. Lanfranchi, P. Peruta, A. Perrone, et al. Toward a New Landscape of Systems Management in an Autonomic Computing Environment. IBM Systems Journal 42, No. 1 (2003).

Digital Library

[18]

M. Peleg, A. Boxwala, O. Ogunyemi, et al. GLIF3: The Evolution of a Guideline Representation Format. In Proceedings of the AMIA Fall Symposium, 2000.

[19]

M. Peleg, S. Tu, J. Bury, et al. Comparing Computer-interpretable Guideline Models: a Case Study Approach. Journal of the American Medical Informatics Association 10(1) (2003) 52--68

[20]

W. Powley, P. Martin. A Reflective Database-Oriented Framework for Autonomic Managers. In Proceedings of International Conference on Autonomic Systems, San Jose, CA, USA, 2006.

Digital Library

[21]

G. Rabinovitch, D. Wiese. Non-linear Optimization of Performance Functions for Autonomic Database Performance Tuning. In Proceedings of The Third International Conference on Autonomic and Autonomous Systems, Athens, 2007.

Digital Library

[22]

M. Sedlmayr, T. Rose, R. Röhring, et al. A workflow approach towards GLIF execution. In Proceedings on Workshop AI Techniques in Healthcare: Evidence-based Guidelines and Protocols. European Conference on Artificial Intelligence (ECAI), Trento, Italy, 2006.

[23]

A. J. Storm, C. Garcia-Arellano, S. Lightstone, et al. Adaptive self-tuning memory in DB2. In Proceedings of VLDB, 2006.

Digital Library

[24]

TPC Benchmark#8482; C. Standard Specification. Revision 5.7. Transaction Processing Performance Council, 2006.

[25]

TPC Benchmark#8482; H. Standard Specification Revision 2.1.0. Transaction Processing Performance Council, 2003.

[26]

D. Wang, M. Peleg, S. Tu, et al. Design and implementation of the GLIF3 guideline execution engine. Journal of Biomedical Informatics 37, 5 (Oct. 2004).

Digital Library

[27]

D. Wang, M. Peleg, D. Bu, et al. GESDOR - a Generic Execution Model for Sharing of Computer-interpretable Clinical Practice Guidelines. AMIA Annual Symposium Proceedings. 2003:694--8.

[28]

G. Weikum, A. C. Konig, A. Kraiss, et al. Towards Self Tuning Memory Management for Data Servers. Data Engineering Journal 22, 2 (1999) pp 3--11.

[29]

G. Weikum, A. Moenkeberg, C. Hasse, et al. Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering. In Proceedings of VLDB, 2002.

Digital Library

Cited By

Mozaffari MDignös AGamper JStörl U(2024)Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and TuningACM Computing Surveys10.1145/3665323Online publication date: 17-May-2024
https://doi.org/10.1145/3665323
Gunasekaran KTiwari KAcharya R(2023)Utilizing deep learning for automated tuning of database management systems2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI)10.1109/CCCAI59026.2023.00022(75-81)Online publication date: Jun-2023
https://doi.org/10.1109/CCCAI59026.2023.00022
Pavlo AButrovich MMa LMenon PLim WVan Aken DZhang W(2021)Make your database system dream of electric sheepProceedings of the VLDB Endowment10.14778/3476311.347641114:12(3211-3221)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476411
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Index Terms

Autonomic tuning expert: a framework for best-practice oriented autonomic database tuning
1. Information systems
  1. Data management systems
    1. Database administration
    2. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

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Published In

cover image ACM Other conferences

CASCON '08: Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds

October 2008

357 pages

ISBN:9781450378826

DOI:10.1145/1463788

Conference Chairs:
Joanna Ng
Head of the Centre for Advanced Studies
,
Christian Couturier
National Research Council Canada
,
Editors:
Marsha Chechik
University of Toronto
,
Mark Vigder
National Research Council Canada
,
Darlene Stewart
National Research Council Canada
,
Program Chairs:
Mark Vigder
National Research Council Canada
,
Marsh Chechik
University of Toronto

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IBM Toronto Software Lab
IBM Centers for Advanced Studies (CAS)

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2008

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Cited By

Mozaffari MDignös AGamper JStörl U(2024)Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and TuningACM Computing Surveys10.1145/3665323Online publication date: 17-May-2024
https://doi.org/10.1145/3665323
Gunasekaran KTiwari KAcharya R(2023)Utilizing deep learning for automated tuning of database management systems2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI)10.1109/CCCAI59026.2023.00022(75-81)Online publication date: Jun-2023
https://doi.org/10.1109/CCCAI59026.2023.00022
Pavlo AButrovich MMa LMenon PLim WVan Aken DZhang W(2021)Make your database system dream of electric sheepProceedings of the VLDB Endowment10.14778/3476311.347641114:12(3211-3221)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476411
Li GZhou XSun JYu XHan YJin LLi WWang TLi S(2021)openGaussProceedings of the VLDB Endowment10.14778/3476311.347638014:12(3028-3042)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476380
Zhang JZhou KLi GLiu YXie MCheng BXing J(2021)$$\hbox {CDBTune}^{+}$$: An efficient deep reinforcement learning-based automatic cloud database tuning systemThe VLDB Journal10.1007/s00778-021-00670-9Online publication date: 5-Jun-2021
https://doi.org/10.1007/s00778-021-00670-9
Zhang JLiu YZhou KLi GXiao ZCheng BXing JWang YCheng TLiu LRan MLi ZBoncz PManegold SAilamaki ADeshpande AKraska T(2019)An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement LearningProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300085(415-432)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300085
Bobrov NChernishev GNovikov B(2017)Workload-Independent Data-Driven Vertical PartitioningNew Trends in Databases and Information Systems10.1007/978-3-319-67162-8_27(275-284)Online publication date: 9-Sep-2017
https://doi.org/10.1007/978-3-319-67162-8_27
Bobrov NChernishev GGrigoriev DNovikov B(2017)An Evaluation of TANE Algorithm for Functional Dependency DetectionModel and Data Engineering10.1007/978-3-319-66854-3_16(208-222)Online publication date: 6-Sep-2017
https://doi.org/10.1007/978-3-319-66854-3_16
BAO XXIAO NLU YCHEN Z(2016)A Configuration Management Study to Fast Massive Writing for Distributed NoSQL SystemIEICE Transactions on Information and Systems10.1587/transinf.2016EDP7104E99.D:9(2269-2282)Online publication date: 2016
https://doi.org/10.1587/transinf.2016EDP7104
Dawoud WTakouna IMeinel C(2011)Elastic VM for rapid and optimum virtualized resources' allocation2011 5th International DMTF Academic Alliance Workshop on Systems and Virtualization Management: Standards and the Cloud (SVM)10.1109/SVM.2011.6096465(1-4)Online publication date: Oct-2011
https://doi.org/10.1109/SVM.2011.6096465
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