%0 Conference Proceedings
%T Self-tunable DBMS Replication with Reinforcement Learning
%+ Universidade do Minho = University of Minho  [Braga]
%A Ferreira, Luís
%A Coelho, Fábio
%A Pereira, José
%Z Part 4: Machine Learning for Systems
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 20th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS)
%C Valletta, Malta
%Y Anne Remke
%Y Valerio Schiavoni
%I Springer International Publishing
%3 Distributed Applications and Interoperable Systems
%V LNCS-12135
%P 131-147
%8 2020-06-15
%D 2020
%R 10.1007/978-3-030-50323-9_9
%K Reinforcement learning
%K Dependability
%K Replication
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-03223253/document
%2 https://inria.hal.science/hal-03223253/file/495624_1_En_9_Chapter.pdf
%L hal-03223253
%U https://inria.hal.science/hal-03223253
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-DAIS
%~ IFIP-LNCS-12135