Stream: zP&C
Time: 12:00 - 13:00
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Critical applications in most data centers are based on CICS transactions. For this reason, it’s important to be immediately alerted as soon as critical transactions start having performance issues.
In this presentation we will show how machine learning algorithms can be used to highlight, in real time, anomalies in the CPU used and in the elapsed time of each CICS transaction execution.
There is currently no attachment for Machine Learning algorithms to highlight CICS transactions anomalies
Matteo Bottazzi has been working as a software developer for EPV Technologies since 2010
with the goal of supporting performance and capacity planning analysts on IBM mainframe.
He worked on several migration projects to help customers modernizing their applications.
He's a regular speaker at International Conferences.
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