TideWatch: Fingerprinting the cyclicality of big data workloads

Daniel W. Williams, Shuai Zheng, Xiangliang Zhang, Hani T. Jamjoom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2014 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2031-2039
Number of pages9
ISBN (Print)9781479933600
DOIs
StatePublished - Apr 2014

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