Improving Datacenter Networking Operations with Large Language Models and Chat Operations

Maintaining a sizable global network requires a vast number of human resources, who are knowledgeable about networks and capable of responding swiftly to various situations. To reduce the burden and number of engineers required to monitor and operate these enormous and complicated networks, artificial intelligence provides an extra tool for managing complex jobs, processing data, and making choices. The usage of large language models has been demonstrated to be crucial in maintaining Service Level Agreements (SLAs), reducing Mean Time to Repair (MTTR), and enhancing the overall availability of large-scale networks, such as those found at Microsoft Azure, Google GCP, and Amazon AWS. Our methodology centres on establishing an interface that leverages natural language processing (NLP) and chat operations (chatops) to facilitate the diagnosis and recovery of services between human engineers and large-scale networks. This approach has proven to improve the ability of engineers to support complex issues and support a greater volume of escalated cases. Ultimately, this has improved MTTR and the overall availability of large-scale data centre networks. The chatops approach reduces the number of tools with which engineers operate daily, thus decreasing the chances of introducing errors during maintenance or troubleshooting processes.