Automation is the key to maintaining optimal network performance and availability during tumultuous times. A resilient, automated network keeps functioning even if administrators can’t physically access the infrastructure or when a recession forces companies to reduce their IT workforce. A network automation framework includes all the tools, technologies, and practices required to build a resilient and fully automated enterprise network infrastructure.
The four building blocks of a resilient network automation framework include:
In previous blogs, we focused on the building blocks that enable network automation and orchestration. In this blog, we’ll discuss how AIOps and machine learning help teams manage their automation and orchestration—and the massive amounts of data produced by their automated systems—more efficiently.
What is AIOps?
AIOps—artificial intelligence for IT operations—was originally introduced by Gartner in 2017. It uses AI technologies like machine learning (ML) and natural language processing (NLP) to analyze IT operations data. This data is pulled in from many different sources, including monitoring and visibility platforms, environmental monitoring sensors, event logs, and firewalls. AIOps utilizes that data to automate tasks like event correlation, anomaly detection, and root cause