Advanced Techniques for Log Analytics in Cloud Operations
Keywords:
Log analytics, cloud operations, machine learning, real-time monitoring, distributed log processing, anomaly detection, security, automation, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
Log analytics plays a crucial role in optimizing cloud operations by enabling proactive monitoring, anomaly detection, and predictive analytics. Traditional log analysis techniques face challenges due to the high volume, velocity, and variety of log data generated in cloud environments. Advanced techniques such as machine learning-based log anomaly detection, distributed log processing, and real-time analytics provide improved insights for operational efficiency This paper explores cutting-edge methodologies for log analytics in cloud operations, focusing on scalable architectures, automated log classification, and statistical correlation techniques. The research includes simulation-based experiments to evaluate performance improvements achieved using advanced log analytics. Findings indicate that intelligent log processing significantly reduces operational downtime and enhances security incident response.
