Predictions Overview

Predictions Overview (Beta)

Predictive Analytics uses machine learning (ML) techniques to forecast possible outliers in resource utilization patterns or equipment failures. This feature helps the administrator to proactively address issues and prevent possible outages.

The Apstra Edge device receives data about the network devices through the configured probes on Apstra. The Edge device sends this data to DC Assurance where the ML models perform the following tasks:

• Aggregate data points received from the network

• Train the ML models using these data points and learn normal behavior to create a baseline

• Forecast future data points by using trained ML models on historical data

• Identify deviations in the forecasted data by using the outlier detection model trained on historical metric data

The Predictive Analytics feature identifies outliers in the following metrics:

• System Health

System Health

The Predictive Analytics feature identifies outliers in the system CPU utilization and system memory utilization metrics. It uses the data collected by the Stream Receivers probe on Apstra to learn and forecast system health metrics and identify deviations. This feature allows you to proactively identify issues in the network infrastructure and resolve them before they impact network traffic.

Note:

You can run the pre-flight check for the Edge device and verify whether the stream receivers are configured correctly. See Pre-Flight Checks for more information.

Predictive Analytics uses the following ML algorithms to predict System Health outliers:

Table 1: Machine Learning Algorithms for System Health

Function	Machine Learning Algorithm	Description
Forecasting	Light Gradient Boost Machine (LGBM)	An ensemble method based on decision trees that combines multiple weak models to produce a single strong prediction. Gradient boosting builds the model sequentially, with each model focusing on errors made by the previous one.
Outlier Detection	Isolation Forest (iForest)	A tree-based method that builds an ensemble of random trees for outlier detection. Isolation tree-based methods recursively select random variables and random split values for these variables as tree nodes at each step to create the subtrees and eventually isolate the outliers at tree leaves.

Note:

Predictive Analytics uses ML algorithms to analyze the data it receives from the network to predict network behavior. Once this feature has been enabled, it requires a certain amount of data points to train its model on expected behavior and to recognize outliers. During the initial period after enabling this feature, the predictions about the system health in the network might not be available or fully accurate. This is because the received data is insufficient for the ML algorithm to analyze, and make meaningful predictions. The accuracy of the predictions improve over time as more data is available for analysis.

View Predicted Impacts

To view the predictive outliers in the network, navigate to Assurance > Predictions. Use the site drop-down to select a specific site.

The Predictions tab displays a list of outliers, the affected device, the severity level of the outlier, the predicted time when the event might occur. It also displays the number of impacted clients and services in the selected site.

Note:

The Predictive Analytics feature forecasts system health outliers that might occur in a 24-hour period.

You can click the clients and services button to view and search from the full list of impacted clients.

You can also use the Predictive Search option on the top right of the page to search for a specific service and view its historical, current, and predicted metrics.

If no predictive outliers are displayed, it could be because of the following reasons:

• The network devices are behaving as expected.

• The Predictive Analytics feature is newly configured and not enough data is available yet to make reliable predictions.

• The streaming receivers are not sending data from the Edge device to DC Assurance. In this case, verify whether the probes are configured correctly on Apstra.

If the list of impacted clients and services for a predicted outlier is not displayed, it could be because:

• No traffic is flowing through the affected devices in the network.

• DC Assurance is not receiving traffic information from the Edge device. In this case, run the pre-flight check for the Edge device and verify if the flow servers are configured correctly. See Pre-Flight Checks for more information.

When you select an outlier on the Predictions tab and click View topology, the network topology is displayed showing the device and the services impacted by the predicted outlier.

View Topology and Device Data

The network topology displays the impacted services and clients for the selected outlier along with the traffic flow between the services and the network devices as shown in Figure 3.

When you select a device from the topology, the right pane displays options to view more details about the predicted anomalies.

For System Health, the graph displays the historical, current, and predicted CPU and memory usage data for the selected device.

Click the Now button to scroll to the current metrics for the device on the plotted graph. You can have the graph display data aggregated in 1-min or 1-hour intervals.

If an outlier is detected in the predicted data points, it is highlighted in purple on the graph. Hover over the outlier to view details such as the type of outlier, the severity, the predicted start and end time of the outlier, and so on.

Benefits of Predictions

The Predictive Analytics feature provides the following benefits:

• Provides an early warning to administrators about possible outliers.

• Helps understand the impact of predicted outliers.

• Enables administrators to proactively prevent potential outages.