KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Machine Intelligence & kNowledge Engineering Lab
Permanent link to this recordhttp://hdl.handle.net/10754/564894
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AbstractIntrinsic 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.
CitationWilliams, 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
Conference/Event name33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014