Top

Published in:

2020 | OriginalPaper | Chapter

Self-tunable DBMS Replication with Reinforcement Learning

Authors : Luís Ferreira, Fábio Coelho, José Pereira

Published in: Distributed Applications and Interoperable Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Fault-tolerance is a core feature in distributed database systems, particularly the ones deployed in cloud environments. The dependability of these systems often relies in middleware components that abstract the DBMS logic from the replication itself. The highly configurable nature of these systems makes their throughput very dependent on the correct tuning for a given workload. Given the high complexity involved, machine learning techniques are often considered to guide the tuning process and decompose the relations established between tuning variables.

This paper presents a machine learning mechanism based on reinforcement learning that attaches to a hybrid replication middleware connected to a DBMS to dynamically live-tune the configuration of the middleware according to the workload being processed. Along with the vision for the system, we present a study conducted over a prototype of the self-tuned replication middleware, showcasing the achieved performance improvements and showing that we were able to achieve an improvement of 370.99% on some of the considered metrics.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Kollaps/Thunderstorm: Reproducible Evaluation of Distributed Systems

next chapter DroidAutoML: A Microservice Architecture to Automate the Evaluation of Android Machine Learning Detection Systems

Apache Distributed Log (2018). http://bookkeeper.apache.org/distributedlog/. Accessed 19 July 2019

Agrawal, S., Chaudhuri, S., Narasayya, V.R.: Automated selection of materialized views and indexes in SQL databases. In: VLDB, pp. 496–505 (2000)

Curino, C., Jones, E., Zhang, Y., Madden, S.: Schism: a workload-driven approach to database replication and partitioning. Proc. VLDB Endow. 3(1–2), 48–57 (2010)CrossRef

Dias, K., Ramacher, M., Shaft, U., Venkataramani, V., Wood, G.: Automatic performance diagnosis and tuning in oracle. In: CIDR, pp. 84–94. CIDR (2005)

Duan, S., Thummala, V., Babu, S.: Tuning database configuration parameters with ituned. Proc. VLDB Endow. 2(1), 1246–1257 (2009)CrossRef

French, C.D.: “One size fits all" database architectures do not work for DSS. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, SIGMOD 1995, pp. 449–450. ACM, New York (1995). https://doi.org/10.1145/223784.223871

Garcıa, J., Fernández, F.: A comprehensive survey on safe reinforcement learning. J. Mach. Learn. Res. 16(1), 1437–1480 (2015)MathSciNetMATH

George, L.: HBase: The Definitive Guide: Random Access to your Planet-Size Data. O’Reilly Media Inc., Sebastopol (2011)

Hintjens, P.: ZeroMQ: Messaging for many Applications. O’Reilly Media Inc., Sebastopol (2013)

10.

Li, G., Zhou, X., Li, S., Gao, B.: Qtune: a query-aware database tuning system with deep reinforcement learning. Proc. VLDB Endow. 12(12), 2118–2130 (2019)CrossRef

11.

Marcus, R., Papaemmanouil, O.: Deep reinforcement learning for join order enumeration. In: Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2018, pp. 3:1–3:4. ACM, New York (2018). https://doi.org/10.1145/3211954.3211957

12.

Morff, A.R., Paz, D.R., Hing, M.M., González, L.M.G.: A reinforcement learning solution for allocating replicated fragments in a distributed database. Computación y Sistemas 11(2), 117–128 (2007)

13.

Powell, D.: Delta-4: A Generic Architecture for Dependable Distributed Computing, vol. 1. Springer, Cham (2012). https://doi.org/10.1007/978-3-642-84696-0CrossRef

14.

Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (2014)MATH

15.

Schiefer, K.B., Valentin, G.: Db2 universal database performance tuning. IEEE Data Eng. Bull. 22(2), 12–19 (1999)

16.

Schnaitter, K., Abiteboul, S., Milo, T., Polyzotis, N.: Colt: continuous on-line tuning. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD 2006, pp. 793–795. ACM, New York (2006). https://doi.org/10.1145/1142473.1142592

17.

Stonebraker, M., Rowe, L.A.: The Design of Postgres, vol. 15. ACM, New York (1986)

18.

Storm, A.J., Garcia-Arellano, C., Lightstone, S.S., Diao, Y., Surendra, M.: Adaptive self-tuning memory in db2. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 1081–1092. VLDB Endowment (2006)

19.

Sullivan, D.G., Seltzer, M.I., Pfeffer, A.: Using Probabilistic Reasoning to Automate Software Tuning, vol. 32. ACM, New York (2004)

20.

Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)MATH

21.

Trummer, I., Moseley, S., Maram, D., Jo, S., Antonakakis, J.: Skinnerdb: regret-bounded query evaluation via reinforcement learning. Proc. VLDB Endow. 11(12), 2074–2077 (2018). https://doi.org/10.14778/3229863.3236263CrossRef

22.

Valentin, G., Zuliani, M., Zilio, D.C., Lohman, G., Skelley, A.: Db2 advisor: an optimizer smart enough to recommend its own indexes. In: Proceedings of 16th International Conference on Data Engineering (Cat. no. 00CB37073), pp. 101–110. IEEE (2000)

23.

Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)MATH

24.

Wiesmann, M., Pedone, F., Schiper, A., Kemme, B., Alonso, G.: Understanding replication in databases and distributed systems. In: Proceedings 20th IEEE International Conference on Distributed Computing Systems, pp. 464–474. IEEE (2000)

25.

Zhang, J., et al.: An end-to-end automatic cloud database tuning system using deep reinforcement learning. In: Proceedings of the 2019 International Conference on Management of Data, pp. 415–432 (2019)

Title: Self-tunable DBMS Replication with Reinforcement Learning
Authors: Luís Ferreira
Fábio Coelho
José Pereira
Publisher: Springer International Publishing
Book: Distributed Applications and Interoperable Systems
Print ISBN: 978-3-030-50322-2

Electronic ISBN: 978-3-030-50323-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-50323-9_9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner