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An Experiment on Bare-Metal BigData Provisioning
Ata Turk, Boston University; Ravi S. Gudimetla, Northeastern University; Emine Ugur Kaynar, Jason Hennessey, and Sahil Tikale, Boston University; Peter Desnoyers, Northeastern University; Orran Krieger, Boston University
Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.
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