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Network Insights - Resources

Company: Cisco
3132 downloads

Details

In a world where data is key and where data quality means the difference between success and failure, the Network Insights - Resources application (NIR) gives you the reliability you need to make decisions and troubleshoot quickly.
It provides a way to gather resource information through data collection to provide an overview of available resources and their active processes and configurations across the entire APIC.
Using this data, you can make crucial business decisions that give you more usable information in a shorter time, which can lead to being more operationally effective.

Data quality is highly dependent upon the parts that gather and analyze it. As a robust tool, NIR has many parts that seamlessly work to create higher quality data, but its key components are as follows.

Data Collection: The data that you need is stored and always available by using software or hardware agents within the fabric devices. In this process, telemetry data is first streamed. This streamed data is then is processed through the analytics pipeline and is stored in the data lake. The data then resides in the data lake until it’s used by your organization as required.

There are many pieces of telemetry information collected from various devices in the fabric in order to achieve the highest data quality. Among the many pieces of information, the most crucial ones are as follows.

Anomaly Detection: This involves understanding the behavior of each fabric component using different machine learning algorithms. When the resource behavior deviates from the expected pattern, anomalies are raised.

Resources Analytics: These analytics include monitoring of software and hardware resources such as CPU, memory, VRF, BD, EPG to ensure that they are being used optimally.

Environmental: This information covers external factors such as fans, power, and temperature.

Event Analytics: These analytics monitor events, faults, configuration changes, and other events. They are then correlated to create a clearer picture of why the faults were raised, and provide information for troubleshooting.