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    Towards automatic parameter tuning of stream processing systems

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    Type
    Conference Paper
    Authors
    Bilal, Muhammad
    Canini, Marco cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2017-09-27
    Online Publication Date
    2017-09-27
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/626125
    
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    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.
    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).
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the 2017 Symposium on Cloud Computing - SoCC '17
    Conference/Event name
    2017 Symposium on Cloud Computing, SoCC 2017
    DOI
    10.1145/3127479.3127492
    Additional Links
    https://dl.acm.org/citation.cfm?doid=3127479.3127492
    ae974a485f413a2113503eed53cd6c53
    10.1145/3127479.3127492
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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