ISCAS OpenIR
mapreduce framework optimization via performance modeling
Xu Lijie
2012
Conference Name2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
SourceProceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
Pages2506-2509
Conference DateMay 21, 2012 - May 25, 2012
Conference PlaceShanghai, China
Indexed TypeEI ; ISTP
ISSN2164-7062
ISBN9780769546766
Department(1) Institute of Software Chinese Academy of Sciences China; (2) Graduate University Chinese Academy of Sciences China
English AbstractMapReduce framework has become the state-of-the-art paradigm for large-scale data processing. In our ongoing work, we attempt to solve the three interrelated problems: how to build an accurate MapReduce performance model, how to use it to automatically detect and optimize slow-running MapReduce jobs, and how to use it to help scheduler arrange job execution sequence. Currently, we mainly study the job execution time model and its training method. We also present several policies to optimize the job configuration and scheduler. © 2012 IEEE.; MapReduce framework has become the state-of-the-art paradigm for large-scale data processing. In our ongoing work, we attempt to solve the three interrelated problems: how to build an accurate MapReduce performance model, how to use it to automatically detect and optimize slow-running MapReduce jobs, and how to use it to help scheduler arrange job execution sequence. Currently, we mainly study the job execution time model and its training method. We also present several policies to optimize the job configuration and scheduler. © 2012 IEEE.
KeywordData Processing Distributed Parameter Networks Scheduling
SponsorshipIEEE Computer Society Technical Committee on Parallel Processing
SubjectComputer Science
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/15782
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Xu Lijie. mapreduce framework optimization via performance modeling[C],2012:2506-2509.
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