Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/19170
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lu, Jiaheng | - |
dc.contributor.author | Chen, Yuxing | - |
dc.contributor.author | Herodotou, Herodotos | - |
dc.contributor.author | Babu, Shivnath | - |
dc.date.accessioned | 2020-10-16T05:20:26Z | - |
dc.date.available | 2020-10-16T05:20:26Z | - |
dc.date.issued | 2019-08 | - |
dc.identifier.citation | Proceedings of the VLDB Endowment, 2019, vol. 12, no. 12, pp. 1970-1973 | en_US |
dc.identifier.issn | 21508097 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/19170 | - |
dc.description.abstract | Database and big data analytics systems such as Hadoop and Spark have a large number of configuration parameters that control memory distribution, I/O optimization, parallelism, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators struggle to understand and tune them to achieve good performance. In this tutorial, we review existing approaches on automatic parameter tuning for databases, Hadoop, and Spark, which we classify into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We describe the foundations of different automatic parameter tuning algorithms and present pros and cons of each approach. We also highlight real-world applications and systems, and identify research challenges for handling cloud services, resource heterogeneity, and real-time analytics. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Proceedings of the VLDB Endowment | en_US |
dc.rights | © ACM | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Mapreduce | en_US |
dc.subject | Optimization | en_US |
dc.subject | Management | en_US |
dc.subject | Tuning algorithms | en_US |
dc.subject | Real-time analytics | en_US |
dc.title | Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems | en_US |
dc.type | Article | en_US |
dc.collaboration | University of Helsinki | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Duke University | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Finland | en_US |
dc.country | Cyprus | en_US |
dc.country | United States | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.14778/3352063.3352112 | en_US |
dc.relation.issue | 12 | en_US |
dc.relation.volume | 12 | en_US |
cut.common.academicyear | 2018-2019 | en_US |
dc.identifier.spage | 1970 | en_US |
dc.identifier.epage | 1973 | en_US |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
crisitem.journal.journalissn | 2150-8097 | - |
crisitem.journal.publisher | ACM | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-8717-1691 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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