Behnaz Arzani, Selim Ciraci, Boon Thau Loo, Assaf Schuster, Geoff Outhred. Proceedings of the 2016 ACM SIGCOMM Conference.<\/p>\n
<\/p>\n
Today, root cause analysis of failures in data centers is mostly
\ndone through manual inspection. More often than not, customers
\nblame the network as the culprit. However, other
\ncomponents of the system might have caused these failures.
\nTo troubleshoot, huge volumes of data are collected over the
\nentire data center. Correlating such large volumes of diverse
\ndata collected from different vantage points is a daunting
\ntask even for the most skilled technicians.
\nIn this paper, we revisit the question: how much can you
\ninfer about a failure in the data center using TCP statistics
\ncollected at one of the endpoints? Using an agent that captures
\nTCP statistics we devised a classification algorithm that
\nidentifies the root cause of failure using this information at
\na single endpoint. Using insights derived from this classification
\nalgorithm we identify dominant TCP metrics that
\nindicate where\/why problems occur in the network. We validate
\nand test these methods using data that we collect over
\na period of six months in the Azure production cloud.<\/p>\n","protected":false},"excerpt":{"rendered":"
Taking the Blame Game out of Data Centers Operations with NetPoirot Behnaz Arzani, Selim Ciraci, Boon Thau Loo, Assaf Schuster, Geoff Outhred. Proceedings of the 2016 ACM SIGCOMM Conference. Today, root cause analysis of failures in data centers is mostly done through manual inspection. 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