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Automated Diagnosis Without Predictability Is a Recipe for Failure
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HotCloud '12 | TaPP '12 | ||
WiAC '12 | USENIX ATC '12 | ||
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Raja R. Sambasivan and Gregory R. Ganger, Carnegie Mellon University
Automated management is critical to the success of cloud computing, given its scale and complexity. But, most systems do not satisfy one of the key properties required for automation: predictability, which in turn relies upon low variance. Most automation tools are not effective when variance is consistently high. Using automated performance diagnosis as a concrete example, this position paper argues that for automation to become a reality, system builders must treat variance as an important metric and make conscious decisions about where to reduce it. To help with this task, we describe a framework for reasoning about sources of variance in distributed systems and describe an example tool for helping identify them.
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author = {Raja R. Sambasivan and Gregory R. Ganger},
title = {Automated Diagnosis Without Predictability Is a Recipe for Failure},
booktitle = {4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 12)},
year = {2012},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotcloud12/workshop-program/presentation/sambasivan},
publisher = {USENIX Association},
month = jun
}
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