Enhancing Microservice Reliability in Cloud Environments Using Machine Learning for Anomaly Detection

Microservices-based applications benefit from rapid growth and development in cloud environments with container technologies such as Docker. However, assuring the reliability of these microservices is now a top priority for application providers. Anomaly detection techniques are important because they can discover activities that could lead to unexpected failures. To guarantee excellent service quality-virtualization services necessitate advanced dependability and availability measures. Traditional monitoring systems identify overloads and outages-and they automatically scale services or disguise errors. However, these systems frequently overlook the oddities and errors that precede faults. To close this gap-we proposed to use Machine Learning (ML) methods to identify suspicious activity by evaluating measurements from all levels and components of the cloud architecture. This strategy seeks to discover issues before they cause outages-hence-improving service dependability and availability. Offline evaluations of data from anomaly injection experiments revealed that these ML models have extremely high accuracy, precision, and recall values. This suggests that the proposed approach is extremely effective in detecting abnormalities and preventing future failures-hence-increasingthe reliability of microservices in cloud environments.