Cite this DOI

Subbarao Duggisetty (2026). AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments. *Journal of Science & Technology*, *11*(04), 1. https://doi.org/10.46243/jst.2026.v11.i04.pp01-15

@article{subbaraoduggisetty2026aipowered,
  author    = {Subbarao Duggisetty},
  title     = {{AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments}},
  journal   = {Journal of Science \& Technology},
  year      = {2026},
  month     = {apr},
  volume    = {11},
  number    = {04},
  pages     = {1},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2026.v11.i04.pp01-15},
  url       = {https://doi.org/10.46243/jst.2026.v11.i04.pp01-15},
  language  = {en},
  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.}
}

TY  - JOUR
TI  - AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments
AU  - Subbarao Duggisetty
JO  - Journal of Science & Technology
PY  - 2026
DA  - 2026/04/27/
VL  - 11
IS  - 04
SP  - 1
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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.
DO  - 10.46243/jst.2026.v11.i04.pp01-15
UR  - https://doi.org/10.46243/jst.2026.v11.i04.pp01-15
ER  -

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    "URL": "https://doi.org/10.46243/jst.2026.v11.i04.pp01-15",
    "title": "AI-Powered Chatbot Solution for Efficient Network Troubleshooting in Hybrid Cloud Environments",
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    "language": "en",
    "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.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2026.v11.i04.pp01-15 gives all four in one JSON answer.