A New Intelligent Event Correlation paradigm in HetNets: A Case Study of ServiceNow’s AIOps Capabilities

Event Monitoring in Heterogeneous networks (HetNets) is extremely challenging due to the diverse network technologies and protocols involved. This study addresses the common challenge of event correlation in Heterogeneous Networks (HetNets). Which is crucial for ensuring network reliability and efficiency. In HetNets, we propose a novel way to integrate ServiceNow’s AIOps platform with advanced machine learning techniques to enhance correlation accuracy and reduce response times. Based on our findings, the proposed methodology outperforms existing solutions, resulting in significant improvements in both network performance and reliability. The research encompasses the integration of the ServiceNow platform with real-time HetNet of a big FinTech company. The platform ingests real-time data, advanced analytics, and predictive intelligence algorithms to detect, group, classify, and respond to network events. Key performance metrics, such as event detection accuracy, response time, and resource utilization, were systematically measured and analyzed. The findings indicate that the ServiceNow platform substantially enhances event correlation capabilities of HetNets. Significant improvements were observed in event detection accuracy and reduced response times when compared to distributed monitoring systems. The platform’s adaptability to different network configurations and its scalable architecture are important advantages. Additionally, the integration of machine learning techniques augments predictive intelligence capabilities, thereby reducing network downtime and enhancing automation and overall reliability. In conclusion, the ServiceNow platform’s advanced event monitoring mechanism provides a robust solution for heterogeneous networks. Future research will aim to further refine the machine learning models and expand the platform’s capabilities to support emerging network technologies.