International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Powered Monitoring and Predictive Maintenance for Cloud Infrastructure: Leveraging AWS CloudWatch and ML

Author(s) Naga Surya Teja Thallam
Country United States
Abstract The IT infrastructure domain benefits from cloud computing because it delivers customizable resources available on demand. Creating reliable cloud system operations continues to be difficult because dynamic workload changes clash with unpredictable system failures and the intricate nature of distributed architectures. Monitoring methods relying on static thresholds together with rule-based alerts deliver reactive responses but they do not produce sufficient disruption prevention. The research investigates how AI facilitates predictive maintenance for cloud systems with the help of AWS CloudWatch combined with machine learning algorithms for advanced failure prediction and anomaly detection. This research introduces a framework that uses a combination of supervised and unsupervised ML models for AWS CloudWatch metrics and logs processing through Amazon SageMaker and AI analytics to deliver real-time monitoring and proactive fault prevention. The research shows how AI-enabled predictive maintenance cuts down both Mean Time to Detect (MTTD) and Mean Time to Repair (MTTR) leading to better resource use while decreasing service interruptions. Composite AI solutions alongside improved IoT integration and explainable AI systems are rising as potential solutions to overcome data quality, scalability issues and security concerns in AI monitoring. The next phase of investigation needs to prioritize improved computational precision and security to advance predictive maintenance methods for cloud services systems.
Keywords Cloud computing, AI-driven monitoring, predictive maintenance, AWS CloudWatch, anomaly detection, machine learning, system reliability, proactive fault prevention, AI in IT operations, cloud infrastructure optimization
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-12
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 3Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals