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    TideWatch: Fingerprinting the cyclicality of big data workloads

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    Type
    Conference Paper
    Authors
    Williams, Daniel W.
    Zheng, Shuai
    Zhang, Xiangliang cc
    Jamjoom, Hani T.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2014-04
    Permanent link to this record
    http://hdl.handle.net/10754/564894
    
    Metadata
    Show full item record
    Abstract
    Intrinsic to 'big data' processing workloads (e.g., iterative MapReduce, Pregel, etc.) are cyclical resource utilization patterns that are highly synchronized across different resource types as well as the workers in a cluster. In Infrastructure as a Service settings, cloud providers do not exploit this characteristic to better manage VMs because they view VMs as 'black boxes.' We present TideWatch, a system that automatically identifies cyclicality and similarity in running VMs. TideWatch predicts period lengths of most VMs in Hadoop workloads within 9% of actual iteration boundaries and successfully classifies up to 95% of running VMs as participating in the appropriate Hadoop cluster. Furthermore, we show how TideWatch can be used to improve the timing of VM migrations, reducing both migration time and network impact by over 50% when compared to a random approach. © 2014 IEEE.
    Citation
    Williams, D., Zheng, S., Zhang, X., & Jamjoom, H. (2014). TideWatch: Fingerprinting the cyclicality of big data workloads. IEEE INFOCOM 2014 - IEEE Conference on Computer Communications. doi:10.1109/infocom.2014.6848144
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE INFOCOM 2014 - IEEE Conference on Computer Communications
    Conference/Event name
    33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014
    ISBN
    9781479933600
    DOI
    10.1109/INFOCOM.2014.6848144
    ae974a485f413a2113503eed53cd6c53
    10.1109/INFOCOM.2014.6848144
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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