by Val Vaduva, CTO Enghouse Networks


In the not-too-distant future, network operations centre (NOC) engineers around the world will be able to predict network behavior versus react to service disrupting network failures.

With a Predictive Service Assurance System in place, NOC engineers will be empowered to predict and preemptively address network issues based on trends and established preventative maintenance protocols. Improved level of system intelligence and control will also drive a more productive and creative service assurance work environment.

The Enghouse Networks team has developed a number of AI use cases, and more are being added to our extensive product portfolio of over 50 applications that encompass OSS, BSS and VAS solutions.

Device incidence by time. Actual vs. predicted.

In one of our AI enabled Service Assurance portfolio use cases shown above, the gray line represents AI-predicted network events, and the blue line indicates the actual events that were observed in the customer’s network. Where there is no blue line, the prediction matched actual events that occurred.

Enghouse’s Service Assurance platform has proven very reliable in producing accurate results, which is essential for any type of machine learning system to work. When paired with intelligent, self-tuning algorithms, our platform can achieve a high level of predictive accuracy. This is also the framework for closed loop automation which can analyze and then respond to network events in service assurance environments.

The end result for the service providers is a network that is more reliable, automated and cost efficient to run.