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Fingerprinting Data Center Offense for Crisis Detection

Explore crisis identification through data center fingerprinting methods, analyzing detection methods and recovery impact. Understand the configuration and crisis types in a Windows-based data center study with limited data.

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Fingerprinting Data Center Offense for Crisis Detection

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  1. Fingerprinting the Datacenter Offense Mykell Miller, Gautam Bhawsar

  2. Crisis Detection • Why don’t you detect unidentified crises? • How severe is each crisis? • May affect recovery method • May affect detection probability and method • Useful for operators to know • Fingerprints are averaged over epochs in the crisis • Everywhere else you use the median

  3. Time to identification • What are you doing for 10 minutes? • Why have the time between epochs be 15 minutes? • Will shorter lead to more accurate or quicker identification?

  4. Data Center • What is the configuration? • Fat tree, etc. • What kind of servers? • Type of servers affects type and frequency of crises • Using only Windows • Study cannot be applied to other OS

  5. Data Set • Only 19 examples • 8 types of crises had only 1 instance each • Training set of only 5 examples • No statistics on the user application being run • Don’t know if it’s representative

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