Advancing autonomous networking: an in-depth look at Forward Predict

Forward has introduced a tool called Forward Predict, designed to improve network management by modelling the potential impact of network changes before they are deployed.

It is built around a Network Digital Twin, which is a model of device behaviour across both network and application layers. This model is used to identify possible policy conflicts and to estimate how the network is likely to behave under different conditions.

Forward Predict uses this digital twin to simulate proposed changes in a controlled environment that mirrors the production network. The aim is to detect potential issues before deployment and provide analysis of likely outcomes, helping organisations assess risk in advance.

The system is intended to support:

• Faster design-to-deployment processes
• Pre-production validation of network changes
• Identification of risks earlier in the planning stage

The approach is based on the idea that production networks should not be used directly for testing changes, as this can lead to operational risk. In traditional workflows, network changes often require significant preparation, but outcomes may only become fully apparent after deployment.

Common issues associated with this traditional approach include:

• Network outages that require remediation
• Security problems discovered after deployment
• Delays in implementing critical changes

Forward Predict is positioned as part of a broader direction in networking towards more automated or autonomous management systems, where network changes can be evaluated and processed with reduced manual intervention.

The development builds on the company’s earlier work on digital twin modelling and related systems aimed at more predictive, model-based network analysis.