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As data centers and end users become increasingly reliant on virtualization technology, more efficient and accurate methods of profiling such systems are needed. However, under virtualization the virtual machine and OS each try to manage the same resources independently, the underlying hardware is now multiplexed between many streams of execution, and non-trivial interference can be caused by seemingly unrelated resources. While sampling techniques are effective at gathering average behaviors over long runs, understanding the time-varying behavior of programs under virtualization, the correlation between events at the level of program phases, or the transient effects of rare events requires a new way of profiling virtualized applications.
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