Artificial Intelligence Self-Healing Capability Assessment in Microservices Applications deployed in AWS using Cloud watch and Hystrix
Keywords:
Microservice, Cloud Migration, Containerization Distributed Systems, Microservice SecurityAbstract
Microservices architecture has gained significant traction in modern software development due to its scalability and flexibility. However, maintaining the reliability and availability of microservices applications in dynamic cloud environments remains a challenge. In this paper, we investigate the effectiveness of artificial intelligence (AI)-driven self-healing capabilities in microservices applications deployed on Amazon Web Services (AWS), utilizing AWS CloudWatch for monitoring and Hystrix for fault tolerance. We begin with a comprehensive literature review, examining existing self-healing mechanisms in microservices and previous research on AI-driven fault detection and recovery. Additionally, we provide an overview of AWS CloudWatch's monitoring features and Hystrix's role in enhancing fault tolerance. Our methodology involves the assessment of self-healing capabilities using predefined criteria, implemented through experimentation in an AWS environment. We describe the setup of microservices architecture, configuration of CloudWatch alarms, and integration of Hystrix for fault tolerance. Furthermore, we detail the implementation of AI algorithms for real-time analysis of monitoring data. Through empirical results and analysis, we demonstrate the efficacy of AI-driven self-healing in detecting and mitigating faults in microservices applications. We compare the performance of AI-driven approaches with traditional methods, highlighting the advantages and limitations of each. Additionally, we evaluate the effectiveness of CloudWatch and Hystrix in maintaining system health. This research contributes to the understanding of AI-driven self-healing capabilities in microservices applications, providing insights into the practical implementation and assessment of self-healing mechanisms in dynamic cloud environments. Our findings offer valuable implications for enhancing the resilience and reliability of microservices architectures in modern software systems.
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