IJCT Best Paper Award 2024

IJCT Best Paper Award 2024

Recognizing Excellence in Computer Science Research

Awarded Paper

Title: Using Artificial Intelligence and AIOps for Automated Fault Prediction and Prevention in Cloud Native Settings

Journal: International Journal of Computer Techniques (IJCT)
Volume/Issue: Volume 11, Issue 6, December 2024
ISSN: 2394-2231
Journal Website: https://ijctjournal.org/

Authors:
Balajee Asish Brahmandam, Independent Researcher, Austin, Texas, USA (balajeeasish@utexas.edu)
Srinath Chandramohan, Independent Researcher, Dallas, Texas, USA (srinathittouch@gmail.com)

Award Announcement

Congratulations to the authors! The editorial board of the International Journal of Computer Techniques is proud to present the IJCT Best Paper Award 2024 to this outstanding research article. This paper was selected for its exceptional contribution to the field of cloud computing, artificial intelligence, and IT operations, setting a new benchmark for innovation and practical impact.

Why this paper was selected:

Groundbreaking Innovation: The paper introduces a novel AIops-driven method for proactive fault prediction and prevention in cloud native environments, addressing real-world challenges faced by modern IT infrastructures.
Technical Excellence: By integrating machine learning with AIOps, the authors present a robust, automated solution for system health management, which is both scalable and adaptable.
Significant Impact: The research demonstrates measurable improvements in downtime reduction, problem detection accuracy, and resource optimization, making it highly relevant for industry adoption.
Clarity and Relevance: The work is well-structured, clearly written, and offers practical insights for both researchers and practitioners in the field.
Advancing the Field: The approach sets a new direction for future research in automated IT operations and intelligent cloud management.

Summary of the Award-Winning Research

Extremely dynamic, cloud native ecosystems-especially those based on microservices architecture-feature intricate interdependencies and constant load and resource fluctuations. These environments often have unanticipated problems that lead to worse performance, outages, and higher running expenses. Because conventional monitoring systems find it difficult to forecast and prevent such occurrences in real time, adopting more proactive strategies is vital.

This work offers a defect prediction method driven by artificial intelligence combined with AIOps to actively identify and avoid faults in cloud native systems. The model uses machine learning methods to examine past system data, forecast possible failures, and start automated corrective steps such as resource scaling or traffic rerouting. Experimental findings show that the suggested method greatly minimizes system downtime, increases the precision of problem detection, and optimizes resource consumption. This work, which presents a unique approach for including machine learning-based defect prediction into AIOps systems, provides a more automated and efficient tool for controlling system health in dynamic cloud native settings.

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