Automatic configuration of the Cassandra database using irace
Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to...
Saved in:
| Published in: | PeerJ. Computer science Vol. 7; p. e634 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
PeerJ. Ltd
05.08.2021
PeerJ, Inc PeerJ Inc |
| Subjects: | |
| ISSN: | 2376-5992, 2376-5992 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained. |
|---|---|
| AbstractList | Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained. Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained. |
| ArticleNumber | e634 |
| Audience | Academic |
| Author | Franzin, Alberto Silva-Muñoz, Moisés Bersini, Hugues |
| Author_xml | – sequence: 1 givenname: Moisés surname: Silva-Muñoz fullname: Silva-Muñoz, Moisés – sequence: 2 givenname: Alberto orcidid: 0000-0002-4066-0375 surname: Franzin fullname: Franzin, Alberto – sequence: 3 givenname: Hugues surname: Bersini fullname: Bersini, Hugues |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34435094$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkslrGzEUh4eS0qRpbj2XgV5a6LgajdZDCsZ0MQQKXc5Clp4mMmPJlWZK-99XttNgh0gHbd_7vUXveXUWYoCqetmiGectf78FSOvG5BnryJPqAnecNVRKfHa0P6-ucl4jhFraliGfVecdIR1FklxU1_NpjBs9elObGJzvp1QOMdTR1eMt1Audsw426drqUa90hnrKPvS1T9rAi-qp00OGq7v1svr56eOPxZfm5uvn5WJ-0xjK5dhYKTRquUPCUSeYlhyM09Iw5LgQzAJzhnEHGInWWEFXmEgCrgXWUo4l7i6r5UHXRr1W2-Q3Ov1VUXu1v4ipVzqVHAZQCFlBjKDEFg1iWfGCudYggchVCadofThobafVBqyBMCY9nIievgR_q_r4W4mOMsZ2wby5E0jx1wR5VBufDQyDDhCnrDBlxTWWHSno6wfoOk4plFIVigrMug4dUb0uCfjgYvFrdqJqzjjiFLUdKtTsEapMCxtf_g6cL_cnBm9PDAozwp-x11POavn92yn76rgo99X43ygFeHcATIo5J3D3SIvUrhXVvhWVyYrtE8cPcOPHfWOVoP3wuNE_1Z_gnw |
| CitedBy_id | crossref_primary_10_1145_3643751 |
| Cites_doi | 10.14778/3329772.3329780 10.1007/978-3-319-09333-8_1 10.5220/0005846400490056 10.1016/j.orp.2016.09.002 10.1016/j.ejor.2019.01.018 10.1016/j.jnca.2016.01.010 10.1145/1012888.1005739 10.1007/978-3-319-13021-7_6 10.1007/978-3-319-92639-1_60 10.14778/3352063.3352129 10.1109/TKDE.2020.2994641 10.14778/3339490.3339503 10.1007/s12530-013-9072-y 10.1007/978-3-319-62410-5_11 10.1145/1353452.1353455 10.1023/A:1006556606079 10.1109/ACCESS.2020.2990735 10.1613/jair.2861 10.1007/978-3-319-44406-2_12 10.1155/2015/502795 10.14778/1687627.1687767 10.1016/j.suscom.2019.01.017 10.14778/2732977.2732995 10.14778/3352063.3352112 10.14778/3192965.3192971 |
| ContentType | Journal Article |
| Copyright | 2021 Silva-Muñoz et al. COPYRIGHT 2021 PeerJ. Ltd. 2021 Silva-Muñoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Silva-Muñoz et al. 2021 Silva-Muñoz et al. |
| Copyright_xml | – notice: 2021 Silva-Muñoz et al. – notice: COPYRIGHT 2021 PeerJ. Ltd. – notice: 2021 Silva-Muñoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2021 Silva-Muñoz et al. 2021 Silva-Muñoz et al. |
| DBID | AAYXX CITATION NPM ISR 3V. 7XB 8AL 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- M0N P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.7717/peerj-cs.634 |
| DatabaseName | CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central Korea ProQuest Central Student SciTech Collection (ProQuest) ProQuest Computer Science Collection Computer Science Database Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Acceso a contenido Full Text - Doaj |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2376-5992 |
| ExternalDocumentID | oai_doaj_org_article_00d84c854d4944d69c627aae9e49bc57 PMC8356662 A670750130 34435094 10_7717_peerj_cs_634 |
| Genre | Journal Article |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: CHIST-ERA-17-BDSI-001 ABIDI – fundername: 2018-SHAPE-25a |
| GroupedDBID | 53G 5VS 8FE 8FG AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO FRP GNUQQ GROUPED_DOAJ H13 HCIFZ IAO ICD IEA ISR ITC K6V K7- M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM 3V. ARCSS M0N NPM 7XB 8AL 8FK JQ2 PKEHL PQEST PQUKI PRINS PUEGO Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c579t-d98a017f08f5f86a97ecfa9c60f7886de6fc67fe2081cd85b2494ef1e61572923 |
| IEDL.DBID | P5Z |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000701706000003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2376-5992 |
| IngestDate | Fri Oct 03 12:35:22 EDT 2025 Tue Nov 04 02:03:38 EST 2025 Thu Oct 02 03:59:15 EDT 2025 Sun Sep 07 03:31:59 EDT 2025 Tue Nov 11 10:17:31 EST 2025 Tue Nov 04 17:56:13 EST 2025 Thu Nov 13 14:23:07 EST 2025 Thu Jan 02 22:39:41 EST 2025 Tue Nov 18 22:29:51 EST 2025 Sat Nov 29 02:15:31 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Databases Automatic configuration Parameter tuning Cassandra Hyperparameter tuning |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 2021 Silva-Muñoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c579t-d98a017f08f5f86a97ecfa9c60f7886de6fc67fe2081cd85b2494ef1e61572923 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4066-0375 |
| OpenAccessLink | https://www.proquest.com/docview/2558263304?pq-origsite=%requestingapplication% |
| PMID | 34435094 |
| PQID | 2558263304 |
| PQPubID | 2045934 |
| PageCount | e634 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_00d84c854d4944d69c627aae9e49bc57 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8356662 proquest_miscellaneous_2564942934 proquest_journals_2558263304 gale_infotracmisc_A670750130 gale_infotracacademiconefile_A670750130 gale_incontextgauss_ISR_A670750130 pubmed_primary_34435094 crossref_primary_10_7717_peerj_cs_634 crossref_citationtrail_10_7717_peerj_cs_634 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-05 |
| PublicationDateYYYYMMDD | 2021-08-05 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Diego – name: San Diego, USA |
| PublicationTitle | PeerJ. Computer science |
| PublicationTitleAlternate | PeerJ Comput Sci |
| PublicationYear | 2021 |
| Publisher | PeerJ. Ltd PeerJ, Inc PeerJ Inc |
| Publisher_xml | – name: PeerJ. Ltd – name: PeerJ, Inc – name: PeerJ Inc |
| References | Raasveldt (10.7717/peerj-cs.634/ref-49) 2018 Mahgoub (10.7717/peerj-cs.634/ref-38) 2017b Pagnozzi (10.7717/peerj-cs.634/ref-44) 2019; 276 Babu (10.7717/peerj-cs.634/ref-4) 2009 Mahgoub (10.7717/peerj-cs.634/ref-36) 2017a Birattari (10.7717/peerj-cs.634/ref-10) 2002 Duan (10.7717/peerj-cs.634/ref-19) 2009; 2 Pushak (10.7717/peerj-cs.634/ref-47) 2018 Marcus (10.7717/peerj-cs.634/ref-40) 2019 Kraska (10.7717/peerj-cs.634/ref-26) 2018 Ma (10.7717/peerj-cs.634/ref-34) 2018 Oh (10.7717/peerj-cs.634/ref-43) 2005 Krishnan (10.7717/peerj-cs.634/ref-27) 2018 Stützle (10.7717/peerj-cs.634/ref-56) 2013 Aniceto (10.7717/peerj-cs.634/ref-3) 2015; 2015 Pérez Cáceres (10.7717/peerj-cs.634/ref-48) 2017 Abramova (10.7717/peerj-cs.634/ref-1) 2014 Kwan (10.7717/peerj-cs.634/ref-29) 2003 Bergstra (10.7717/peerj-cs.634/ref-7) 2011; 24 Debnath (10.7717/peerj-cs.634/ref-16) 2008 Valentin (10.7717/peerj-cs.634/ref-61) 2000 Mahgoub (10.7717/peerj-cs.634/ref-37) 2020 Daz (10.7717/peerj-cs.634/ref-15) 2016; 67 Haughian (10.7717/peerj-cs.634/ref-22) 2016 Wang (10.7717/peerj-cs.634/ref-63) 2014; 8807 Jindal (10.7717/peerj-cs.634/ref-25) 2018; 11 Van Aken (10.7717/peerj-cs.634/ref-62) 2017 Miranda (10.7717/peerj-cs.634/ref-42) 2014 Li (10.7717/peerj-cs.634/ref-31) 2019; 12 Hutter (10.7717/peerj-cs.634/ref-23) 2011 Zilio (10.7717/peerj-cs.634/ref-74) 2004 Schnaitter (10.7717/peerj-cs.634/ref-51) 2007 Birattari (10.7717/peerj-cs.634/ref-9) 2004 Yuan (10.7717/peerj-cs.634/ref-67) 2020 Zheng (10.7717/peerj-cs.634/ref-70) 2014; 8588 Dutt (10.7717/peerj-cs.634/ref-21) 2019; 12 Dias (10.7717/peerj-cs.634/ref-17) 2005 Kuhlenkamp (10.7717/peerj-cs.634/ref-28) 2014; 7 Zhou (10.7717/peerj-cs.634/ref-71) 2020 Wei (10.7717/peerj-cs.634/ref-65) 2014 Chavan (10.7717/peerj-cs.634/ref-13) 2011 Lu (10.7717/peerj-cs.634/ref-32) 2019; 12 Sheng (10.7717/peerj-cs.634/ref-52) 2019 López-Ibáñez (10.7717/peerj-cs.634/ref-33) 2016; 3 Swaminathan (10.7717/peerj-cs.634/ref-58) 2016 Abubakar (10.7717/peerj-cs.634/ref-2) 2014; 7 Bergstra (10.7717/peerj-cs.634/ref-8) 2012; 13 Baik (10.7717/peerj-cs.634/ref-5) 2019 Bao (10.7717/peerj-cs.634/ref-6) 2018 Zhang (10.7717/peerj-cs.634/ref-69) 2019 Storm (10.7717/peerj-cs.634/ref-55) 2006 Tan (10.7717/peerj-cs.634/ref-59) 2019; 12 Hutter (10.7717/peerj-cs.634/ref-24) 2009; 36 Zhu (10.7717/peerj-cs.634/ref-73) 2017b Zhu (10.7717/peerj-cs.634/ref-72) 2017a Stillger (10.7717/peerj-cs.634/ref-54) 2001; 1 Mahgoub (10.7717/peerj-cs.634/ref-39) 2019 Maron (10.7717/peerj-cs.634/ref-41) 1997; 11 Wang (10.7717/peerj-cs.634/ref-64) 2012 Le (10.7717/peerj-cs.634/ref-30) 2014 Pedrozo (10.7717/peerj-cs.634/ref-45) 2018; 10870 Zhang (10.7717/peerj-cs.634/ref-68) 2012 Wu (10.7717/peerj-cs.634/ref-66) 2019 Cooper (10.7717/peerj-cs.634/ref-14) 2010 Rodd (10.7717/peerj-cs.634/ref-50) 2013; 4 Cao (10.7717/peerj-cs.634/ref-11) 2018 Duarte (10.7717/peerj-cs.634/ref-20) 2016; 2 Silva-Muñoz (10.7717/peerj-cs.634/ref-53) 2020 Mahajan (10.7717/peerj-cs.634/ref-35) 2019; 22 Cassandra (10.7717/peerj-cs.634/ref-12) 2014 Tran (10.7717/peerj-cs.634/ref-60) 2008; 4 Pinheiro (10.7717/peerj-cs.634/ref-46) 2017; 620 Dou (10.7717/peerj-cs.634/ref-18) 2020; 8 Sullivan (10.7717/peerj-cs.634/ref-57) 2004; 32 |
| References_xml | – volume: 12 start-page: 1044 issue: 9 year: 2019 ident: 10.7717/peerj-cs.634/ref-21 article-title: Selectivity estimation for range predicates using lightweight models publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3329772.3329780 – start-page: 11 year: 2008 ident: 10.7717/peerj-cs.634/ref-16 article-title: Sard: a statistical approach for ranking database tuning parameters – start-page: 489 year: 2018 ident: 10.7717/peerj-cs.634/ref-26 article-title: The case for learned index structures – volume: 8588 start-page: 1 year: 2014 ident: 10.7717/peerj-cs.634/ref-70 article-title: Self-tuning performance of database systems with neural network doi: 10.1007/978-3-319-09333-8_1 – volume: 2 start-page: 49 year: 2016 ident: 10.7717/peerj-cs.634/ref-20 article-title: Cassandra for internet of things: an experimental evaluation publication-title: International Conference on Internet of Things and Big Data doi: 10.5220/0005846400490056 – start-page: 189 year: 2020 ident: 10.7717/peerj-cs.634/ref-37 article-title: {OPTIMUSCLOUD}: heterogeneous configuration optimization for distributed databases in the cloud – start-page: 11 year: 2002 ident: 10.7717/peerj-cs.634/ref-10 article-title: A racing algorithm for configuring metaheuristics – volume: 3 start-page: 43 issue: 1 year: 2016 ident: 10.7717/peerj-cs.634/ref-33 article-title: The irace package: iterated racing for automatic algorithm configuration publication-title: Operations Research Perspectives doi: 10.1016/j.orp.2016.09.002 – volume: 276 start-page: 409 issue: 2 year: 2019 ident: 10.7717/peerj-cs.634/ref-44 article-title: Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2019.01.018 – volume: 67 start-page: 99 issue: 7 year: 2016 ident: 10.7717/peerj-cs.634/ref-15 article-title: State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing publication-title: Journal of Network and Computer applications doi: 10.1016/j.jnca.2016.01.010 – year: 2009 ident: 10.7717/peerj-cs.634/ref-4 article-title: Automated experiment-driven management of (database) systems – start-page: 13 year: 2014 ident: 10.7717/peerj-cs.634/ref-12 article-title: Apache cassandra – start-page: 893 year: 2018 ident: 10.7717/peerj-cs.634/ref-11 article-title: Towards better understanding of black-box auto-tuning: a comparative analysis for storage systems – volume: 32 start-page: 404 issue: 1 year: 2004 ident: 10.7717/peerj-cs.634/ref-57 article-title: Using probabilistic reasoning to automate software tuning publication-title: ACM SIGMETRICS Performance Evaluation Review doi: 10.1145/1012888.1005739 – start-page: 415 year: 2019 ident: 10.7717/peerj-cs.634/ref-69 article-title: An end-to-end automatic cloud database tuning system using deep reinforcement learning – volume: 8807 start-page: 71 year: 2014 ident: 10.7717/peerj-cs.634/ref-63 article-title: Benchmarking replication and consistency strategies in cloud serving databases: HBase and Cassandra doi: 10.1007/978-3-319-13021-7_6 – start-page: 143 year: 2010 ident: 10.7717/peerj-cs.634/ref-14 article-title: Benchmarking cloud serving systems with YCSB – volume: 10870 start-page: 716 year: 2018 ident: 10.7717/peerj-cs.634/ref-45 article-title: An adaptive approach for index tuning with learning classifier systems on hybrid storage environments doi: 10.1007/978-3-319-92639-1_60 – start-page: 631 year: 2018 ident: 10.7717/peerj-cs.634/ref-34 article-title: Query-based workload forecasting for self-driving database management systems – start-page: 476 year: 2017a ident: 10.7717/peerj-cs.634/ref-36 article-title: Suitability of nosql systems-cassandra and scylladb-for iot workloads – start-page: 202 volume-title: Artificial Evolution: 13th International Conference, E’volution Artificielle, EA 2017; Paris, France, October 25-27, 2017; Revised Selected, volume 10764 of Lecture Notes in Computer Science year: 2017 ident: 10.7717/peerj-cs.634/ref-48 article-title: Automatic configuration of GCC using irace – start-page: 84 year: 2005 ident: 10.7717/peerj-cs.634/ref-17 article-title: Automatic performance diagnosis and tuning in oracle – year: 2019 ident: 10.7717/peerj-cs.634/ref-40 article-title: Neo: a learned query optimizer publication-title: arXiv preprint – start-page: 28 year: 2017b ident: 10.7717/peerj-cs.634/ref-38 article-title: Rafiki: a middleware for parameter tuning of nosql datastores for dynamic metagenomics workloads – volume: 12 start-page: 2118 issue: 12 year: 2019 ident: 10.7717/peerj-cs.634/ref-31 article-title: Qtune: a query-aware database tuning system with deep reinforcement learning publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3352063.3352129 – year: 2020 ident: 10.7717/peerj-cs.634/ref-71 article-title: Database meets artificial intelligence: a survey publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2020.2994641 – start-page: 199 year: 2014 ident: 10.7717/peerj-cs.634/ref-1 article-title: Evaluating cassandra scalability with YCSB – volume: 12 start-page: 1221 issue: 10 year: 2019 ident: 10.7717/peerj-cs.634/ref-59 article-title: iBTune: individualized buffer tuning for large-scale cloud databases publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3339490.3339503 – volume: 4 start-page: 133 issue: 2 year: 2013 ident: 10.7717/peerj-cs.634/ref-50 article-title: Adaptive neuro-fuzzy technique for performance tuning of database management systems publication-title: Evolving Systems doi: 10.1007/s12530-013-9072-y – start-page: 1284 year: 2011 ident: 10.7717/peerj-cs.634/ref-13 article-title: Dbridge: a program rewrite tool for set-oriented query execution – start-page: 374 year: 2019 ident: 10.7717/peerj-cs.634/ref-5 article-title: Bridging the semantic gap with SQL query logs in natural language interfaces to databases – volume: 620 start-page: 87 year: 2017 ident: 10.7717/peerj-cs.634/ref-46 article-title: Smart grids data management: a case for Cassandra doi: 10.1007/978-3-319-62410-5_11 – volume: 4 start-page: 1 issue: 1 year: 2008 ident: 10.7717/peerj-cs.634/ref-60 article-title: A new approach to dynamic self-tuning of database buffers publication-title: ACM Transactions on Storage doi: 10.1145/1353452.1353455 – volume: 7 start-page: 23 issue: 8 year: 2014 ident: 10.7717/peerj-cs.634/ref-2 article-title: Performance evaluation of nosql systems using YCSB in a resource austere environment publication-title: Performance Evaluation – start-page: 223 year: 2019 ident: 10.7717/peerj-cs.634/ref-39 article-title: {SOPHIA}: online reconfiguration of clustered nosql databases for time-varying workloads – start-page: 507 year: 2011 ident: 10.7717/peerj-cs.634/ref-23 article-title: Sequential model-based optimization for general algorithm configuration – start-page: 271 year: 2018 ident: 10.7717/peerj-cs.634/ref-47 article-title: Algorithm configuration landscapes: more benign than expected? – volume: 24 start-page: 2546 year: 2011 ident: 10.7717/peerj-cs.634/ref-7 article-title: Algorithms for hyper-parameter optimization publication-title: Advances in Neural Information Processing Systems – start-page: 459 year: 2007 ident: 10.7717/peerj-cs.634/ref-51 article-title: On-line index selection for shifting workloads – start-page: 325 year: 2014 ident: 10.7717/peerj-cs.634/ref-42 article-title: Fine-tuning of support vector machine parameters using racing algorithms – start-page: 389 year: 2012 ident: 10.7717/peerj-cs.634/ref-68 article-title: A model for application-oriented database performance tuning – year: 2004 ident: 10.7717/peerj-cs.634/ref-9 article-title: The problem of tuning metaheuristics as seen from a machine learning perspective – start-page: 1218 year: 2005 ident: 10.7717/peerj-cs.634/ref-43 article-title: Resource selection for autonomic database tuning – volume: 11 start-page: 193 issue: 1–5 year: 1997 ident: 10.7717/peerj-cs.634/ref-41 article-title: The racing algorithm: model selection for lazy learners publication-title: Artificial Intelligence Review doi: 10.1023/A:1006556606079 – volume: 8 start-page: 80638 year: 2020 ident: 10.7717/peerj-cs.634/ref-18 article-title: Hdconfigor: automatically tuning high dimensional configuration parameters for log search engines publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2990735 – volume: 36 start-page: 267 year: 2009 ident: 10.7717/peerj-cs.634/ref-24 article-title: ParamILS: an automatic algorithm configuration framework publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.2861 – start-page: 1 year: 2018 ident: 10.7717/peerj-cs.634/ref-27 article-title: Learning to optimize join queries with deep reinforcement learning publication-title: arXiv preprint – start-page: 1501 year: 2020 ident: 10.7717/peerj-cs.634/ref-67 article-title: Automatic view generation with deep learning and reinforcement learning – year: 2003 ident: 10.7717/peerj-cs.634/ref-29 article-title: Automatic database configuration for db2 universal database: compressing years of performance expertise into seconds of execution – year: 2020 ident: 10.7717/peerj-cs.634/ref-53 article-title: Supplementaty material for: automatic configuration of the Cassandra database using irace – year: 2016 ident: 10.7717/peerj-cs.634/ref-22 article-title: Benchmarking replication in cassandra and mongodb NoSQL datastores doi: 10.1007/978-3-319-44406-2_12 – start-page: 893 year: 2013 ident: 10.7717/peerj-cs.634/ref-56 article-title: Automatic (offline) configuration of algorithms – start-page: 1223 year: 2019 ident: 10.7717/peerj-cs.634/ref-66 article-title: Designing succinct secondary indexing mechanism by exploiting column correlations – volume: 2015 start-page: 1 issue: 2 year: 2015 ident: 10.7717/peerj-cs.634/ref-3 article-title: Evaluating the cassandra NoSQL database approach for genomic data persistency publication-title: International journal of genomics doi: 10.1155/2015/502795 – start-page: 1081 year: 2006 ident: 10.7717/peerj-cs.634/ref-55 article-title: Adaptive self-tuning memory in db2 – volume: 13 start-page: 281 issue: 1 year: 2012 ident: 10.7717/peerj-cs.634/ref-8 article-title: Random search for hyper-parameter optimization publication-title: The Journal of Machine Learning Research – start-page: 101 year: 2000 ident: 10.7717/peerj-cs.634/ref-61 article-title: Db2 advisor: an optimizer smart enough to recommend its own indexes – start-page: 29 year: 2018 ident: 10.7717/peerj-cs.634/ref-6 article-title: Autoconfig: automatic configuration tuning for distributed message systems – year: 2019 ident: 10.7717/peerj-cs.634/ref-52 article-title: Scheduling oltp transactions via machine learning publication-title: arXiv preprint – volume: 1 start-page: 19 year: 2001 ident: 10.7717/peerj-cs.634/ref-54 article-title: Leo-db2’s learning optimizer – start-page: 338 year: 2017a ident: 10.7717/peerj-cs.634/ref-72 article-title: Bestconfig: tapping the performance potential of systems via automatic configuration tuning – start-page: 1 year: 2018 ident: 10.7717/peerj-cs.634/ref-49 article-title: Fair benchmarking considered difficult: common pitfalls in database performance testing – start-page: 323 year: 2016 ident: 10.7717/peerj-cs.634/ref-58 article-title: Quantitative analysis of scalable nosql databases – start-page: 1332 year: 2012 ident: 10.7717/peerj-cs.634/ref-64 article-title: The nosql principles and basic application of cassandra model – start-page: 1 year: 2017b ident: 10.7717/peerj-cs.634/ref-73 article-title: Acts in need: automatic configuration tuning with scalability guarantees – volume: 2 start-page: 1246 issue: 1 year: 2009 ident: 10.7717/peerj-cs.634/ref-19 article-title: Tuning database configuration parameters with ituned publication-title: Proceedings of the VLDB Endowment doi: 10.14778/1687627.1687767 – start-page: 194 year: 2014 ident: 10.7717/peerj-cs.634/ref-65 article-title: Self-tuning performance of database systems based on fuzzy rules – volume: 22 start-page: 120 year: 2019 ident: 10.7717/peerj-cs.634/ref-35 article-title: Improving the energy efficiency of relational and NoSQL databases via query optimizations publication-title: Sustainable Computing: Informatics and Systems doi: 10.1016/j.suscom.2019.01.017 – start-page: 47 year: 2014 ident: 10.7717/peerj-cs.634/ref-30 article-title: Epc information services with No-SQL datastore for the internet of things – volume: 7 start-page: 1219 issue: 12 year: 2014 ident: 10.7717/peerj-cs.634/ref-28 article-title: Benchmarking scalability and elasticity of distributed database systems publication-title: Proceedings of the VLDB Endowment doi: 10.14778/2732977.2732995 – start-page: 180 year: 2004 ident: 10.7717/peerj-cs.634/ref-74 article-title: Recommending materialized views and indexes with the ibm db2 design advisor – volume: 12 start-page: 1970 issue: 12 year: 2019 ident: 10.7717/peerj-cs.634/ref-32 article-title: Speedup your analytics: automatic parameter tuning for databases and big data systems publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3352063.3352112 – start-page: 1009 year: 2017 ident: 10.7717/peerj-cs.634/ref-62 article-title: Automatic database management system tuning through large-scale machine learning – volume: 11 start-page: 800 issue: 7 year: 2018 ident: 10.7717/peerj-cs.634/ref-25 article-title: Selecting subexpressions to materialize at datacenter scale publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3192965.3192971 |
| SSID | ssj0001511119 |
| Score | 2.2019658 |
| Snippet | Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e634 |
| SubjectTerms | Algorithms Artificial Intelligence Automatic configuration Cassandra Configurations Configurations (Computers) Data Mining and Machine Learning Data processing Database administration Design of experiments Hyperparameter tuning Machine learning Methods Optimization Parameter tuning Parameters Performance evaluation Software Statistical methods Statistical models Workloads |
| SummonAdditionalLinks | – databaseName: Acceso a contenido Full Text - Doaj dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Li9RAEC5k8eDF9yO6SiuKB4mbR6cfBw_j4qKXRXzA3pruTvfsiCRLMvH3W5VkhgkiXrxOV6D7S1Xq-4bqKoCXMngnVObSKiieciWq1JWuSpWLSCiclc6qcdiEPD9XFxf688GoL6oJm9oDT8CdZFmtuFcVr7nmvBbai0JaG3Tg2vlqvEeeSX0gpqb7wfQp0FOlu0TJcnIVQvcj9f1bUfJFDhpb9f_5QT7ISMtqyYP0c3Ybbs68ka2m_d6Ba6G5C7d2MxnYHKL34N1q2LZjG1aGSjdu1sP0ilkbGXI9dopk2TZ1ZxnVhlIOY1T6vmabzvpwH76fffh2-jGdRySkeHS9TWutLMZUzFSsohJWI_bRIkZZRG0r6iCiFzKGAjO_r1XlUG3xEPOARAZpdVE-gKOmbcIjYLl1WWWFy6PyPI-5ilFYfCSU1uk6yATe7EAzfu4fTmMsfhrUEQSxGSE2vjcIcQKv9tZXU9-Mv9i9J_z3NtTtevwBfcDMPmD-5QMJvKC3Z6ifRUMFM2s79L359PWLWQlJpAgzdQKvZ6PY4r69ne8f4OmpBdbC8nhhiQHnl8s7JzFzwPcGlRkKNfpzKIHn-2V6korYmtAOZCNw_8iv0Obh5FP7c5cceStK7QTkwtsWwCxXms3l2A4cOTRq0OLx_0DyCdwoqGiHamKqYzjadkN4Ctf9r-2m756NMfYbOxEujw priority: 102 providerName: Directory of Open Access Journals |
| Title | Automatic configuration of the Cassandra database using irace |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34435094 https://www.proquest.com/docview/2558263304 https://www.proquest.com/docview/2564942934 https://pubmed.ncbi.nlm.nih.gov/PMC8356662 https://doaj.org/article/00d84c854d4944d69c627aae9e49bc57 |
| Volume | 7 |
| WOSCitedRecordID | wos000701706000003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: P5Z dateStart: 20150527 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database (ProQuest) customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: K7- dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: BENPR dateStart: 20150527 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: PIMPY dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEB7RlgMXyhtDiQwCcUCmTuJ9-IBQWrWiQo2iAlLgstpd74YgZAc74chvZ8bZpLUQXLj4kB1Lszuvb9aTGYDnwlnDZWoS5mSWZJKzxAwNS6TxCCiMFkbLdtiEGI_ldJpPwoVbE8oqNz6xddRFZemO_BChLyJhyr7fLn4kNDWKvq6GERo7sEddEmh0w4R9ubxjYeQQ8nW9u8DE5XDhXP0tsc1rPsw6kaht2P-nW74Sl7o1k1eC0On-_7J_C24G-BmP1vpyG6658g7sb0Y7xMHS78Kb0WpZtd1cY0yY_Xy2WmtKXPkYIWN8jJhbl0WtYyoxpVAYUwX9LJ7X2rp78On05OPxuyRMWkgsE_kyKXKp0TR9Kj3zkuscReh1bnnqMUXmhePecuHdAAGELSQzmLRlzvcd4iFE54Phfdgtq9I9hLivTco0N30vbdb3fek91_iKG2qTF05E8Gpz6sqGNuQ0DeO7wnSEZKRaGSnbKJRRBC-21It1-42_0B2RALc01DS7_aGqZyrYoErTQmZWsqxA7rOC4_4GQmuXuyw3eBARPCPxK2qLUVLdzUyvmkadfbhQIy4IW2HAj-BlIPIV8m11-BsD7p46aXUoDzqUaLe2u7zRFBX8RqMu1SSCp9tlepNq4UpXrYiGI_8I05DmwVopt_seZgh_MWOPQHTUtXMw3ZVy_rXtKo5QHFPZwaN_s_UYbgyoqoeKZtgB7C7rlXsC1-3P5bype7AjprIHe0cn48lFr73fwOd7keDz_NdJrzVPXJ-cnU8-_wY_U0Pz |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqggQXypuFAgZRcUCh2axjO4cKLYWqqy0rBEXqzbUde1mEkiXZgPhT_EZm8tg2QnDrget6Io2deXxfdjxDyDPhrOEyNEHsJAuY5HFgRiYOpPEAKIwWRst62ISYzeTJSfJ-g_zq7sJgWWUXE-tAneYWv5HvAvQFJIzs-9XyW4BTo_Df1W6ERmMWU_fzB1C2cm_yBt7vThQdvD3ePwzaqQKBjUWyCtJEajBDH0ofe8l1Aup6nVgeeqCDPHXcWy68iyBZ2lTGBggKc37oIPcDEsVGBxDyL7GRFOhXUxGcfdOJMQAlTX29AKK0u3Su-BLY8iUfsV7mqwcE_JkGzuXBfo3muaR3sPW_Hdd1cq2F13Tc-MMNsuGym2SrG11B20h2i-yNq1Ved6ulNs_8Yl41nkBzTwES033gFDpLC02xhBZTPcUbAnO6KLR1t8mnC9nEHbKZ5Zm7R-hQmzDW3Ay9tGzoh9J7ruERN9ImSZ0YkBfdW1a2bbOO0z6-KqBbaBOqtgllSwU2MSA7a-ll017kL3Kv0WDWMtgUvP4hL-aqjTEqDFPJrIxZCtqzlMP-IqG1SxxLDBzEgDxFc1PY9iPDuqK5rspSTT5-UGMuEDsCoBmQ562Qz0Fvq9trGrB77BTWk9zuSUJcsv3lzjJVGxdLdWaWA_JkvYxPYq1f5vIKZTjoDzAUZO42TrDe94gBvA8TWBE99-gdTH8lW3yuu6YD1QCqHt3_t1qPyZXD43dH6mgymz4gVyOsYMICoXibbK6Kyj0kl-331aIsHtWuT8npRTvPb9ZTmb0 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF5VBSEulDeBAgui4oBMHMf78KFCoSUiKooiHlLFZdld76apkB3sBMRf49cx40daC8GtB67ZsTS7nsf3ObMzhDwTzhouQxMwJ-MglpwFZmhYII0HQGG0MFpWwybEdCqPj5PZFvnV3oXBsso2JlaBOs0tfiPvA_QFJIzsu--bsojZ4fjV8luAE6Twn9Z2nEZtIkfu5w-gb-X-5BDe9V4Ujd98PHgbNBMGAstEsgrSRGowSR9Kz7zkOgHVvU4sDz1QQ5467i0X3kWQOG0qmQGyEjs_cIADAJVi0wMI_5cEcEwsJ5yxz2ffdxgGo6SutRdAmvpL54rTwJYv-TDuZMFqWMCfKeFcTuzWa55LgOOd__norpNrDeymo9pPbpAtl90kO-1IC9pEuFtkf7Re5VUXW2rzzC_m69pDaO4pQGV6AFxDZ2mhKZbWIgSgeHNgTheFtu42-XQhm7hDtrM8c_cIHWgTMs3NwEsbD_xAes81POKG2iSpEz3yon3jyjbt13EKyFcFNAztQ1X2oWypwD56ZG8jvazbjvxF7jUaz0YGm4VXP-TFXDWxR4VhKmMrWZyC9nHKYX-R0NolLk4MHESPPEXTU9gOJEPrmOt1WarJh_dqxAViSgA6PfK8EfI56G11c30Ddo8dxDqSux1JiFe2u9xaqWriZanOTLRHnmyW8UmsAcxcvkYZDvoDPAWZu7VDbPY9jAH2hwmsiI6rdA6mu5ItTqpu6kBBgMJH9_-t1mNyBXxGvZtMjx6QqxEWNmHdENsl26ti7R6Sy_b7alEWj6ooQMmXi_ad373aouE |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automatic+configuration+of+the+Cassandra+database+using+irace&rft.jtitle=PeerJ.+Computer+science&rft.au=Silva-Mu%C3%B1oz%2C+Mois%C3%A9s&rft.au=Franzin%2C+Alberto&rft.au=Bersini%2C+Hugues&rft.date=2021-08-05&rft.issn=2376-5992&rft.eissn=2376-5992&rft.volume=7&rft.spage=e634&rft_id=info:doi/10.7717%2Fpeerj-cs.634&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2376-5992&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2376-5992&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2376-5992&client=summon |