Machine Learning-Based Self-Healing Systems: Automated Failure Detection and Recovery in Microservices

Ravikanth Konda

doi:10.36948/ijfmr.2021.v03i05.43946

Machine Learning-Based Self-Healing Systems: Automated Failure Detection and Recovery in Microservices

Author(s)	Ravikanth Konda
Country	United States
Abstract	The exponential adoption of microservices architecture has revolutionized software development, enabling scalable, modular, and resilient systems. However, the increased complexity of distributed systems also introduces challenges related to fault detection, isolation, and recovery. Traditional methods of fault management are increasingly inadequate due to the dynamic and decentralized nature of microservices. This paper explores a machine learning-based approach to creating self-healing systems capable of automated failure detection and recovery in microservices environments. We discuss the state-of-the-art methodologies, including anomaly detection, predictive analytics, and reinforcement learning, and propose a novel architecture integrating these techniques to enhance system robustness. Our proposed methodology leverages log and metric data for real-time anomaly detection, root cause analysis, and proactive recovery mechanisms. This system integrates both supervised and unsupervised learning algorithms to achieve a continuous learning loop that improves accuracy over time. Moreover, reinforcement learning is applied for policy-based recovery decisions that adapt to evolving failure patterns. These capabilities are essential in modern systems where manual intervention is neither scalable nor reliable for maintaining service quality. The architecture and algorithms are validated through a series of controlled experiments on a simulated Kubernetes-based microservices platform. The experiments demonstrate significant improvements in fault detection precision, diagnostic speed, and system recovery time compared to traditional rule-based systems. In addition, the paper explores the implications of these findings in operational environments, addressing potential overhead, integration challenges, and scalability concerns. Overall, the results indicate that machine learning can serve as a foundational technology in enabling autonomous, resilient microservices.
Field	Engineering
Published In	Volume 3, Issue 5, September-October 2021
Published On	2021-10-09
DOI	https://doi.org/10.36948/ijfmr.2021.v03i05.43946
Short DOI	https://doi.org/g9hm27

View / Download PDF File

E-ISSN 2582-2160

doi

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 7 Isu 2 Cover Page Vol 7 Isu 1 Cover Page Vol 6 Isu 6

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

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

International Journal For Multidisciplinary Research

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

Machine Learning-Based Self-Healing Systems: Automated Failure Detection and Recovery in Microservices

Share this