Using artificial intelligence and AIOps, automated fault prediction and prevention in Cloud Native settings
IJCT Best Journal Insights
International Journal of Computer Techniques – Volume 11 Issue 6, December 2024 | ISSN: 2394-2231
Summary
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.
Full Text – AIops Automated Fault Prediction Cloud Native
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.
References
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