Tuning Performance Of Spark Programs
Keywords
Apache spark; Performance model
Abstract
Along with the explosive growth of data, there is a great demand to speedup the ability to process them. Although there are several platforms such as Spark that have made analysis easier to developers, the performance tuning for such platforms meanwhile becomes complex. In this paper, we propose an efficient performance optimization engine called Hedgehog to evaluate the performance based on 'Law of Diminishing Marginal Utility' and give an optimal configuration setting. The initial experiments show that our optimization can gain 19.6% performance improvement compared to the naive configuration by tuning only 3 parameters.
Publication Date
5-16-2018
Publication Title
Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018
Number of Pages
282-285
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/IC2E.2018.00057
Copyright Status
Unknown
Socpus ID
85048350509 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85048350509
STARS Citation
Zhang, Hong; Liu, Zixia; and Wang, Liqiang, "Tuning Performance Of Spark Programs" (2018). Scopus Export 2015-2019. 9552.
https://stars.library.ucf.edu/scopus2015/9552