Generation of test traffic configuration based on real-world traffic

Abstract

Some embodiments provide a method for generating a test traffic configuration for testing a first network. From a second network, the method receives a set of data streams representing data traffic observed in the second network. The method uses a machine learning engine to analyze the set of data streams in order to determine traffic patterns in the second network. The method generates the test traffic configuration for testing the first network by replicating the traffic patterns of the second network in the first network.

Description

BACKGROUND[0001]Networking is at the heart of application delivery. Modern applications are built with the requirement of anywhere, anytime, any device, and any-scale. Most of such applications work seamlessly in an ideal world, but the behavior of these applications might differ under varying conditions due to changes in infrastructure and diverse traffic patterns. As such, in a world where the number of connected devices is increasing every day, the importance of a solid IT infrastructure is paramount and inevitable. With this rate of growth, it becomes absolutely essential to ensure the scalability and reliability of an advanced application network appliance (e.g., load balancer, virtualized network function, or other devices) that handles traffic for real-world applications.[0002]As such, application deployments (including any load balancer devices that handle traffic for the application) are typically tested before real-world deployment, using a traffic testing tool. Most moder...

AI Technical Summary

Benefits of technology

The patent describes a system that uses machine learning to analyze data to determine traffic patterns on a network. The system applies various techniques to the data to reduce its size and identify patterns of traffic over time. These patterns are then used to create a test traffic configuration for a testing system. The technical effect of this patent is to provide a method for accurately analyzing and predicting network traffic, which can be useful in improving network efficiency and reducing downtime.

Problems solved by technology

Most modern traffic generators are capable of performing stress testing, but lack diversity in traffic generation that one would see in a real-world production environment.

This is not only time consuming but also arduous to scale for multiple applications.

Images