Paper Details
AI-Based Predictive Maintenance and Resilience for Kubernetes Orchestrated Microservices
Authors
Charan Shankar Kummarapurugu
Abstract
The increasing adoption of microservices architec- ture has revolutionized the way cloud-native applications are deployed and managed, with Kubernetes emerging as a leading orchestration platform. However, ensuring the resilience and reli- ability of microservices remains a challenge due to their dynamic and distributed nature. Traditional maintenance approaches are often reactive, leading to potential system failures and down- time. This paper proposes an AI-based approach for predictive maintenance tailored for Kubernetes-orchestrated microservices, aiming to enhance system resilience. The methodology employs machine learning models to predict failure events and optimize maintenance schedules, thereby reducing downtime and improv- ing system performance. Experimental results demonstrate the effectiveness of the proposed model, showing significant improve- ments in service availability and fault tolerance compared to conventional methods.
Keywords
AI, Predictive Maintenance, Resilience, Kuber- netes, Microservices, Machine Learning, Orchestration.
Citation
AI-Based Predictive Maintenance and Resilience for Kubernetes Orchestrated Microservices. Charan Shankar Kummarapurugu. 2023. IJIRCT, Volume 9, Issue 4. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2411033