ZEN Master
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ZEN Master User Guide
Introduction to ZEN Master
Intelligent Data Platform (IDP)
4min
the intelligent data platform (idp) is a module in zen master that collects and analyzes telemetry data to find patterns of interest to users in order to quickly identify issues the result of the analysis is displayed in the incidents and insights modules, where users can visualize data in ways that allow them to quickly identify problems and fix them multi object correlation analysis (moca) idp’s multi object correlation analysis automatically groups errors into incidents, by determining correlations between individual object incidents the incidents are reported when there are 3 or more sources, targets, or broadcasters having errors at the same time that share a common resource (see shared resources definition below), and when at least 50% of the objects that share that resource are experiencing errors at the same time frame of 10 minutes errors that happen on these objects within 10 minutes of a previous error are grouped together into the incident it is possible to have multiple related incidents if there is a period of more than 10 minutes without any errors in some cases, if there are multiple shared resources, the system may also report multiple related incident records the shared resource can be one of the following client network – the network where source or target objects are connected networks are identified by network asn (autonomous system number), country code, and region code for example, us oh 123 is a network in ohio in the united states with asn 123 server network – the network where a broadcaster is connected network path – the connection between a source and a broadcaster broadcaster – the shared zixi broadcaster based on this analysis, idp provides an automatic rca attribution, which specifies the likely root cause of the incident idp displays its analysis in the following modules insights docid\ gikgygywp00kzcbkwxtjd insights are a set of graphs in zen master that enable you to identify outliers and likely areas of instability in your system insights graphs are shown for broadcasters and for channels these graphs make it easy to compare different workflows, showing you where you need to focus your attention graphs show metrics related to packet recovery as well as other broadcaster kpis such as cpu and memory usage incidents docid\ km0i5tnpzuo ygfv6qcvq the incident view groups together related errors into incidents to simplify the process of identifying the common root cause zen master identifies the object on which the initial error occurred and the network on which the channel is transmitted it also suggests a likely cause of the incident incidents can be created manually and/or automatically generated based on the moca analysis, as described above