Utilizing Machine Learning for Intelligent Data Management in Event-Driven Microservices Architectures

Abstract

The following research paper is a case study of three specific machine learning algorithms, LSTM networks, GBMs and RL for managing data in event-driven microservices architectures. This work assesses the performance of these algorithms using factors including anomaly detection, resource consumption prediction, and service coordination concerning such challenges as anomalies. LSTM networks was used in assessment of the anomalous patterns with accuracy reaching 92%, and false positive rate of 5%. The ability of the GBMs was evaluated for its capacity to accurately predict resource requirements, and in turn, minimize resource over-commitment and under-commitment that occurred, achieving 89% accuracy, with a percentage variation of 18% and 14% accordingly. The RL algorithms proved their potential to enhance the decision-making process governing the orchestration of services and failure recovery with the decision-makers achieving 22% increase in decision making accuracy and failure recovery time was reduced to 4. 5 minutes. These algorithms are discussed separately in the next sections with reference to their applications in intelligent data management in business event processing system. These findings are useful to improve the application of these machine learning techniques to increase the performance, utilization of resources and reliability of the system.