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https://hdl.handle.net/20.500.14279/19170
Τίτλος: | Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems | Συγγραφείς: | Lu, Jiaheng Chen, Yuxing Herodotou, Herodotos Babu, Shivnath |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Λέξεις-κλειδιά: | Mapreduce;Optimization;Management;Tuning algorithms;Real-time analytics | Ημερομηνία Έκδοσης: | Αυγ-2019 | Πηγή: | Proceedings of the VLDB Endowment, 2019, vol. 12, no. 12, pp. 1970-1973 | Volume: | 12 | Issue: | 12 | Start page: | 1970 | End page: | 1973 | Περιοδικό: | Proceedings of the VLDB Endowment | Περίληψη: | 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. | URI: | https://hdl.handle.net/20.500.14279/19170 | ISSN: | 21508097 | DOI: | 10.14778/3352063.3352112 | Rights: | © ACM Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Article | Affiliation: | University of Helsinki Cyprus University of Technology Duke University |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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