Type
Conference PaperAuthors
Bilal, MuhammadCanini, Marco

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2017-09-27Online Publication Date
2017-09-27Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/626125
Metadata
Show full item recordAbstract
Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.Citation
Bilal M, Canini M (2017) Towards automatic parameter tuning of stream processing systems. Proceedings of the 2017 Symposium on Cloud Computing - SoCC ’17. Available: http://dx.doi.org/10.1145/3127479.3127492.Sponsors
Muhammad Bilal was supported by a fellowship from the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) program funded by the European Commission (EACEA) (FPA 2012-0030).Conference/Event name
2017 Symposium on Cloud Computing, SoCC 2017Additional Links
https://dl.acm.org/citation.cfm?doid=3127479.3127492ae974a485f413a2113503eed53cd6c53
10.1145/3127479.3127492