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Trends - CRC Errors

alcollin@redhat.com published on 2016-08-26T15:24:19+00:00, last updated 2016-08-29T12:53:51+00:00

CRC (Cyclic Redundancy Check) is a test to ensure data does not become corrupt when sent across networks or storage devices. The test begins by calculating a check value that is based on the data’s contents that will be sent over the network. The check value is recalculated when the data arrives at its destination, and if the recalculated check value differs from the initial check value, then the data has been corrupted.

CRC Errors and RHEL

Red Hat Enterprise Linux (RHEL) will log received CRC errors as well as the number of packets successfully transmitted through the network card. By dividing the number of errors by the number of total transactions we can compute the network card’s CRC error percentage. This allows us to review how severe and often CRC errors are affecting the system. Figure 1 displays how often CRC errors are found on a monthly scale.

Using data to drive Insights

By reviewing historical data, we can measure the distribution of CRC error percentages. Figure 2 below displays the percentile versus CRC error percentage.

This graph shows the severity of CRC error percentages. For example, we can see that if a system has a CRC error percentage of 0.1%, then it is in the 87th percentile among systems encountering CRC errors. Red Hat Insights can use this information to create and improve rules to more accurately identify risk and severity.

To create the most effective rule, we can review the growth of the CRC error percentages per percentile. Figure 3 easily illustrates that around the 95th percentile there is a large jump in growth. To ensure we inform our customers of a potential issue, we set our rule threshold at a CRC error percentage of 1%, thus ensuring we will flag any systems in the upper 93rd percentile of CRC error percentages.

Final Thoughts

Red Hat Insights uses the results of this analysis to finetune our rules to better serve our customers. By mining historical data, Red Hat Insights is able to track not just how common issues are, but how severe they can become. Using the comprehensive support data, Red Hat Insights is able to provide customers with the best possible prescriptive solutions.


About The Author

alcollin@redhat.com's picture Red Hat


Alex is a software engineer specializing in data science for Red Hat Insights. He holds a degree is computer engineering from The Pennsylvania State University, and has had previous engineering roles at IBM and US Airways. Alex currently resides in Raleigh, NC.