AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments

Abstract

Hybrid Cloud environments combining AWS and on-premises infrastructure presents complex networktroubleshooting challenges. Traditional manual diagnostic methods are time-consuming, error-prone, and struggleto correlate logs across distributed systems in real-time. This study addresses the creation of AI-based chatbotapplication to network fault-finding in hybrid cloud systems, involving the AWS CloudWatch, VPC Flow logs and on-premises infrastructure. The chatbot operates with natural language processing (NLP) to instruct users on thetroubleshooting steps on the basis of the historical and live network data. The system increases operational efficiencyand reduces the time to resolution by automating root cause analysis, log correlation and remediation suggestions.Using Anthropic Claude (Sonnet), Lex AI chatbot achieved 99.67% accuracy and reduced Mean Time to Resolution(MTTR) from 47.0 minutes to 40.13 minutes improvement. The chatbot enhances user experience in real-time andinteractive, 24/7 availability, reduce human error, eliminating the necessity to depend on support teams. The papershows how AI can streamline troubleshooting and optimize network diagnostics of hybrid networks with complexarchitectures