Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/19170
Title: Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems
Authors: Lu, Jiaheng 
Chen, Yuxing 
Herodotou, Herodotos 
Babu, Shivnath 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Mapreduce;Optimization;Management;Tuning algorithms;Real-time analytics
Issue Date: Aug-2019
Source: Proceedings of the VLDB Endowment, 2019, vol. 12, no. 12, pp. 1970-1973
Volume: 12
Issue: 12
Start page: 1970
End page: 1973
Journal: Proceedings of the VLDB Endowment 
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.
URI: https://ktisis.cut.ac.cy/handle/10488/19170
ISSN: 2150-8097
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 
Appears in Collections:Άρθρα/Articles

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