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- Change Assigned Interface Map
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- Device Configuration Lifecycle
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- What is Apstra ZTP
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Device Health Analytics Report
This topic provides an overview of the different types of device health reports you can generate in the Apstra GUI. To learn how to generate this report, see Generate an Analytics Report.
The Device Health report analyzes the health of the device. In this report, you can view the health of all the Apstra managed devices in your network.
Inventory Overview Report
The Inventory Overview report shows the number of devices that Apstra manages in your network. This report (Figure 1) shows the device hardware model, system ID, label, network operating system (NOS), average/max memory usage, and average/max CPU usage.
Memory Usage Analysis Reports
Memory Usage Analysis reports show detailed memory usage for all devices and includes charts used to identify memory leaks. This report is useful for capacity planning and identifying usage patterns to predict demand.
Device Memory Usage Chart
The device memory usage chart (Figure 2) shows the top 10 devices in your network and their memory usage over time. For example:
Memory Trending Charts
The Device Memory Trending chart (Figure 3) shows devices with highest memory increments rate.
To help you identify potential memory leaks on devices, we rank them based on changes in memory usage. Device A's memory usage increases 5-fold from 10M to 50M. Device B's usage increases 10% from 100M to 110M. Device C's memory usage decreases from 500M to 490M. Accordingly, we rank device A before device B and eliminate device C.
CPU Usage Analysis
This section provides on-device CPU usage and Apstra telemetry collectors for each device.
Device CPU Usage
The Device CPU usage chart displays the top 10 devices with the highest CPU usage in your network. Figure 4 shows a baseline CPU usage of about 20 percent, with periodic peaks and valleys in the analysis.
CPU Usage Analysis For Devices
The measurement of some metrics, such as CPU usage, might exhibit periodical behaviors due to certain operations repeated at fixed intervals. For example, Apstra device telemetry collectors might issue CLI commands to collect traffic counters every few second, causing device CPU usage to increase. Using FFT (Fast Fourier Transformation), we converted a time domain measurement to one in the frequency domain to identify associations between various frequencies and known periodical operations.
For example, in the frequency domain chart (Figure 5) for a leaf device, the x-axis represents frequency in number per hour. Value 0 means a constant signal, while value 120 means the signal repeats 120 times per hour, with a 30-second period.
The device CPU usage exhibits a periodic pattern at 34.285714285714285 seconds rate which correspond to following Apstra telemetry service collectors: interface_counters.
