Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/72262
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Ferreira, Luís | por |
dc.contributor.author | Coelho, Fábio | por |
dc.contributor.author | Pereira, José | por |
dc.date.accessioned | 2021-04-23T11:09:06Z | - |
dc.date.issued | 2020 | - |
dc.identifier.isbn | 978-3-030-50322-2 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/72262 | - |
dc.description.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. | por |
dc.description.sponsorship | The research leading to these results has received funding from the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014–2020, under grant agreement No. 731218. | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/731218/EU | - |
dc.rights | restrictedAccess | por |
dc.subject | Reinforcement learning | por |
dc.subject | Dependability | por |
dc.subject | Replication | por |
dc.title | Self-tunable DBMS replication with reinforcement learning | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationStartPage | 131 | por |
oaire.citationEndPage | 147 | por |
oaire.citationConferencePlace | Valletta, Malta | por |
oaire.citationVolume | 12135 | por |
dc.identifier.doi | 10.1007/978-3-030-50323-9_9 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
sdum.journal | Lecture Notes in Computer Science | por |
sdum.conferencePublication | Distributed applications and interoperable systems: 20th IFIP WG 6.1 International Conference, DAIS 2020...: proceedings | por |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Ferreira2020_Chapter_Self-tunableDBMSReplicationWit.pdf Acesso restrito! | 2,01 MB | Adobe PDF | Ver/Abrir |