Automated Diagnosis without Predictability is a Recipe for Failure

Proceedings of the 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud '12), June 12-13, 2012, Boston, MA.

Raja R. Sambasivan & Gregory R. Ganger

Electrical & Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213


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 ešective 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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