An Intelligent Agent Using a Q-Learning Method to Allocate Replicated Data in a Distributed Database

Morffi, Abel Rodríguez; González, Luisa Manuela González; Paz, Darien Rosa; Hing, Marisela Mainegra

doi:10.1109/micai.2007.8

Cited by 1 publication

(1 citation statement)

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“…Specifically in the field of database systems, such strategies have started to be applied in the internals of DBMSs to aid query optimisers to outperform baseline optimisation strategies for join ordering [21] and overall query planning [11]. The outcome is usually a sequential set of decisions based on Markov Decision Chains [12,14]. The considered RL technique does not change the optimisation criterion, but rather the how the exploration process is conducted.…”

Section: Related Workmentioning

confidence: 99%

Self-tunable DBMS Replication with Reinforcement Learning

Ferreira

Coelho

Pereira

2020

Distributed Applications and Interoperable Systems

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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.

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Section: Related Workmentioning

confidence: 99%