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Evolving Systems

, Volume 4, Issue 2, pp 133–143 | Cite as

Adaptive neuro-fuzzy technique for performance tuning of database management systems

  • S. F. Rodd
  • U. P. Kulkarni
  • A. R. Yardi
Original Paper

Abstract

A recent trend in database performance tuning is towards self tuning for some of the important benefits like efficient use of resources, improved performance and low cost of ownership that the auto-tuning offers. Most modern database management systems (DBMS) have introduced several dynamically tunable parameters that enable the implementation of self tuning systems. An appropriate mix of various tuning parameters results in significant performance enhancement either in terms of response time of the queries or the overall throughput. The choice and extent of tuning of the available tuning parameters must be based on the impact of these parameters on the performance and also on the amount and type of workload the DBMS is subjected to. The tedious task of manual tuning and also non-availability of expert database administrators (DBAs), it is desirable to have a self tuning database system that not only relieves the DBA of the tedious task of manual tuning, but it also eliminates the need for an expert DBA. Thus, it reduces the total cost of ownership of the entire software system. A self tuning system also adapts well to the dynamic workload changes and also user loads during peak hours ensuring acceptable application response times. In this paper, a novel technique that combines learning ability of the artificial neural network and the ability of the fuzzy system to deal with imprecise inputs are employed to estimate the extent of tuning required. Furthermore, the estimated values are moderated based on knowledgebase built using experimental findings. The experimental results show significant performance improvement as compared to built in self tuning feature of the DBMS.

Keywords

Tuning Response time Neuro-fuzzy Impact factor Database administrator (DBA) Database cache Buffer hit ratio (BHR) 

Notes

Acknowledgments

We deeply acknowledge the support in the form of computing facilities and funding from our esteemed management of Karnataka Law Society, Belgaum, Karnataka. Our thanks are also due to our Principal, Dr. A.S. Deshpande for his support and encouragement. We also acknowledge the contributions of Mr. Sumeet of VIIIth semester B.E., Information Science and Engineering department for his assistance in setting up the laboratory for carrying out the experiments related to this research work. Our thanks are also due to Mr. Moogbasav, Instructor, Computer Center, GIT, for providing us with the necessary support in setting up of the test bed for the experiments.

References

  1. Acedo MA, Molina MA, Silva R, Marciano M, Portilla EA (2009) Authentication review for nodes in wireless sensor networks. Revista Electrónica de Estudios Telemáticos 9(1):1–23Google Scholar
  2. Agarwal S, Bruno N, Chaudhari S (2006) AutoAdmin: self tuning database system technology. In: IEEE data engineering bulletinGoogle Scholar
  3. Agarwal S et al (2007) Automated selection of materialized views and indexes. In: VLDBGoogle Scholar
  4. Chaudhuri S, Weikum G (2006) Foundations of automated database tuning. In: Data engineeringGoogle Scholar
  5. Chen ANK (2006) Robust optimization for performance tuning of modern database systems. Eur J Oper Res 171:412–429CrossRefGoogle Scholar
  6. Cheng SW, Garlan D et al (2006) Architecture based self adaptation in the presence of multiple objectives. In: Proceedings of 2006 international journal of computer systems and engineeringGoogle Scholar
  7. Choudhuri S, Narasayya V (2007) Self tuning database systems: a decade progress. Microsoft ResearchGoogle Scholar
  8. Choudhuri S, Weikum G (2000) Rethinking database system architecture: towards a self tuning RISC style database system. In: VLDB, pp 1–10Google Scholar
  9. Dageville B, Dias K (2006a) Oracle’s self tuning architecture and solutions. In: IEEE data engineering bulletin, vol 29Google Scholar
  10. Dageville B, Dias K (2006b) Oracle’s self tuning architecture and solutions. In: Bulletin of IEEEGoogle Scholar
  11. Debnath BK, Lilja DJ, Mokbel MF (2008) SARD: a statistical approach for ranking database tuning parameters. In: Data engineering workshop, 2008. ICDEW 2008. IEEE 24th international conferenceGoogle Scholar
  12. Holze M, Ritter N (2011) System models for goal-driven self-management in autonomic databases. Data Knowl Eng 70:685–701CrossRefGoogle Scholar
  13. Hullermeier E (2011) Fuzzy sets in machine learning and data mining. Appl Comput 156:387–406Google Scholar
  14. Iglesias JA, Angelov P, Ledezma A, Sanchis A (2010) An evolving classification of agents behaviors: a general approach. Evol Syst 1(3):161–172CrossRefGoogle Scholar
  15. Koopman P (2004) Elements of the self-healing system problem space. In: IEEE data engineering bulletinGoogle Scholar
  16. Leite D, Ballini R, Costa P, Gomide F (2012) Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evol Syst 3(2):65–79CrossRefGoogle Scholar
  17. Liu P (2005) Design and implementation of self healing database system. In: IEEE conferenceGoogle Scholar
  18. Nehme RV (2008) Database, heal thyself. In: Data engineering workshopGoogle Scholar
  19. Peng X, Chen B et al (2012) Self-tuning software systems through dynamic quality tradeoff and value-based feedback control loop. J Syst Softw 85:2707–2719CrossRefGoogle Scholar
  20. Pérez-Cruz JH, Alanis AY, Rubio JJ, Pacheco J (2012) System Identification based on multilayer differential neural networks: a new result. J Appl Math 2012:1–20CrossRefGoogle Scholar
  21. Rabinovitch G, Wiese D (2007) Non-linear optimization of performance functions autonomic database performance tuning. In: IEEE conferenceGoogle Scholar
  22. Rubio JJ (2009) SOFMLS: online self organizing fuzzy modified least square network. IEEE Trans Fuzzy Syst 17(6):1296–1309MathSciNetCrossRefGoogle Scholar
  23. Rubio JJ, Angelov P, Pacheco J (2011) Uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366CrossRefGoogle Scholar
  24. Satish SK, Saraswatipura MK, Shastry SC (2007) DB2 performance enhancements using Materialized Query Table for LUW Systems, 2007. In: ICONS ’07, second international conferenceGoogle Scholar
  25. Storm AJ et al (2006) Adaptive self-tuning of memory in DB2. In: VLDBGoogle Scholar
  26. Tran DN, Huynh PC et al (2008) A new approach to dynamic self-tuning of database buffers. In: ACM transactions on storage, vol 4Google Scholar
  27. Wang S, Summers RM (2012) Machine learning and radiology. Med Image Anal 16:933–951CrossRefGoogle Scholar
  28. Weikum G, Moenkerngerg A et al (1993) Self-tuning database technology and information services: from wishful thing to viable engineering. In: Parallel and distributed information systemGoogle Scholar
  29. Weikum G, Monkenberg A (2002) Self-tuning database technology: from wishful thinking to viable engineering. In: VLDB conference, pp 20–31Google Scholar
  30. Wiese D, Rabinovitch G (2009) Knowledge management in autonomic database performance tuning. In: Proceedings of 3rd International conference on autonomic and autonomous systems, 2007, p 48Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of ISEGogte Institute of TechnologyBelgaumIndia
  2. 2.Department of CSESDMCETDharwadIndia
  3. 3.Walchand CollegeSangliIndia

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