@inproceedings{ba6c27ec75e74cbea0e5628b1fa0db9a,
title = "Towards automatic parameter tuning of stream processing systems",
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.",
author = "Muhammad Bilal and Marco Canini",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 Symposium on Cloud Computing, SoCC 2017 ; Conference date: 24-09-2017 Through 27-09-2017",
year = "2017",
month = sep,
day = "24",
doi = "10.1145/3127479.3127492",
language = "English (US)",
series = "SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing",
publisher = "Association for Computing Machinery (ACM)",
pages = "189--200",
booktitle = "SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing",
address = "United States",
}