AI Case Study

Verizon monitors and optimises network performance in real-tim using machine learning to analyse network interface data

Verizon employs machine learning algorithms to monitor data from network interfaces such as routers and sensors measuring weather conditions and transactional data such as billing records. Using this data, trends are predicted to make adjustments to resource allocation as well as to detect anomalies within the network leading to much better performance.

Industry

Telecommunications

Fixed Line And Integrated Telecommunications Services

Project Overview

"Verizon collects data from a variety of datasources such as interface statistics, environmental statistics, CPU usage on routers etc to constantly monitor the status of its FIOS fiber optic broadband network and proactively identify bottlenecks or wasted capacities using AI. This has also helped detect manufacturing defects in devices.

Verizon employs predictive analytics algorithms to monitor 3GB of data every second streaming from millions of network interfaces – from customers’ routers to an array of sensors gathering temperature and weather data, and software which “listens in” on operational data, such as billing records to identify and predict outliers and take action to subvert outages."

Reported Results

The company claims:

* Prevented 100s of mishaps from happening using predictive maintenance and proactive monitoring
* Real-time monitoring of household connections led them to identify that they were able to provide higher speeds than previously expected

Technology

Function

Information Technology

Network Operations

Background

Verizon, is an American multinational telecommunications conglomerate.

Benefits

Data

"3GB of data every second streaming from millions of network interfaces – from customers’ routers to an array of sensors gathering temperature and weather data"