AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments
DOI:
https://doi.org/10.46243/jst.2026.v11.i04.pp01-15Keywords:
AI chatbot, functional chatbot,, troubleshooting networks, the hybrid cloud, AWS, natural language processing,, operational efficiency,, real-time logs, and root cause analysis.Abstract
Hybrid Cloud environments combining AWS and on-premises infrastructure presents complex network
troubleshooting challenges. Traditional manual diagnostic methods are time-consuming, error-prone, and struggle
to correlate logs across distributed systems in real-time. This study addresses the creation of AI-based chatbot
application 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 the
troubleshooting steps on the basis of the historical and live network data. The system increases operational efficiency
and 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 and
interactive, 24/7 availability, reduce human error, eliminating the necessity to depend on support teams. The paper
shows how AI can streamline troubleshooting and optimize network diagnostics of hybrid networks with complex
architectures.
