Cloud Computing - An Evaluation of Rules of Thumb for Tuning RDBMSs

Tarcizio Alexandre Bini, Marcos Sfair Sunye, Adriano Lange


Cloud computing environments are attractive for IT service provision as they allow for greater flexibility and rationalization of IT infrastructure. In an attempt to benefit from these environments, IT professionals are incorporating legacy Relational Database Management Systems (RDBMSs) in them. However, the design of these legacy systems do not account to the changes in resource availability, present in cloud environments. This work evaluates the use of rules of thumb in RDBMS configuration. Through an evaluation method that simulates concurrent I/O workloads, we analyzed the RDBMS performance under various settings. The results show that well-known configuration rules are inefficient in these environments and that new definitions are necessary to harvest the benefits of cloud computing environments.


  1. (2013). Bonnie++ benchmark. Available at URL:
  2. (2013). Dbt3-tollkit database test 3. Available at URL:
  3. (2013). Postgresql: The world's most advanced open source database.
  4. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., and Brandic, I. (2009). Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst., 25(6):599-616.
  5. Cecchet, E., Singh, R., Sharma, U., and Shenoy, P. (2011). Dolly: virtualization-driven database provisioning for the cloud. SIGPLAN Not., 46(7):51-62.
  6. Council, T. P. P. (2013). Tpc benchmark h - version 2.16.0. Technical report, Transaction Processing Performance Council.
  7. Debnath, B. K., Lilja, D. J., and Mokbel, M. F. (2008). Exploiting the impact of database system configuration parameters: A design of experiments approach. volume 31, pages 3-10.
  8. Delimitrou, C., Sankar, S., Khessib, B., Vaid, K., and Kozyrakis, C. (2012). Time and cost-efficient modeling and generation of large-scale tpcc/tpce/tpch workloads. In Proceedings of the Third TPC Technology conference on Topics in Performance Evaluation, Measurement and Characterization, TPCTC'11, pages 146-162, Berlin, Heidelberg. Springer-Verlag.
  9. Duan, S., Thummala, V., and Babu, S. (2009). Tuning database configuration parameters with ituned. Proc. VLDB Endow., 2(1):1246-1257.
  10. Hsu, W. W., Smith, A. J., and Young, H. C. (2001). I/o reference behavior of production database workloads and the tpc benchmarks an analysis at the logical level. ACM Trans. Database Syst., 26(1):96-143.
  11. Rao, J., Bu, X., Xu, C.-Z., Wang, L., and Yin, G. (2009). Vconf: a reinforcement learning approach to virtual machines auto-configuration. In Proceedings of the 6th international conference on Autonomic computing, ICAC 7809, pages 137-146, New York, NY, USA. ACM.
  12. Smith, G. (2010). PostgreSQL 9.0 High Performance, chapter Server Configuration Tuning, pages 125-149. Packt Publishing, Limited.
  13. Smith, J. E. and Nair, R. (2005). The architecture of virtual machines. Computer, 38(5):32-38.
  14. Soror, A. A., Aboulnaga, A., and Salem, K. (2007). Database virtualization: A new frontier for database tuning and physical design. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop, ICDEW 7807, pages 388-394, Washington, DC, USA. IEEE Computer Society.
  15. Soror, A. A., Minhas, U. F., Aboulnaga, A., Salem, K., Kokosielis, P., and Kamath, S. (2008). Automatic virtual machine configuration for database workloads. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, SIGMOD 7808, pages 953-966, New York, NY, USA. ACM.
  16. Storm, A. J., Garcia-Arellano, C., Lightstone, S. S., Diao, Y., and Surendra, M. (2006). Adaptive self-tuning memory in db2. In Proceedings of the 32nd international conference on Very large data bases, VLDB 7806, pages 1081-1092. VLDB Endowment.
  17. Tran, D. N., Huynh, P. C., Tay, Y. C., and Tung, A. K. H. (2008). A new approach to dynamic self-tuning of database buffers. Trans. Storage, 4(1):3:1-3:25.
  18. Xiong, P. (2012). Dynamic management of resources and workloads for rdbms in cloud: a control-theoretic approach. In Proceedings of the on SIGMOD/PODS 2012 PhD Symposium, PhD 7812, pages 63-68, New York, NY, USA. ACM.

Paper Citation

in Harvard Style

Bini T., Sunye M. and Lange A. (2014). Cloud Computing - An Evaluation of Rules of Thumb for Tuning RDBMSs . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 187-192. DOI: 10.5220/0004882601870192

in Bibtex Style

author={Tarcizio Alexandre Bini and Marcos Sfair Sunye and Adriano Lange},
title={Cloud Computing - An Evaluation of Rules of Thumb for Tuning RDBMSs},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Cloud Computing - An Evaluation of Rules of Thumb for Tuning RDBMSs
SN - 978-989-758-027-7
AU - Bini T.
AU - Sunye M.
AU - Lange A.
PY - 2014
SP - 187
EP - 192
DO - 10.5220/0004882601870192