Abstract
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.
| Original language | English (US) |
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| Title of host publication | SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 189-200 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781450350280 |
| DOIs | |
| State | Published - Sep 24 2017 |
| Event | 2017 Symposium on Cloud Computing, SoCC 2017 - Santa Clara, United States Duration: Sep 24 2017 → Sep 27 2017 |
Publication series
| Name | SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing |
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Conference
| Conference | 2017 Symposium on Cloud Computing, SoCC 2017 |
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| Country/Territory | United States |
| City | Santa Clara |
| Period | 09/24/17 → 09/27/17 |
Bibliographical note
Publisher Copyright:© 2017 Association for Computing Machinery.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science