Authors: Lakshmi Menon, Aravind Krishnan, Ramya K, Vineeth Das
Abstract: Modern IT environments, characterized by hybrid infrastructure, rapid virtualization, and regulatory constraints, demand sophisticated systems management platforms that go beyond manual operations. Oracle Enterprise Manager Ops Center (OEMOC) has long served as a unified platform for provisioning, patching, asset discovery, and monitoring in Oracle Solaris and Linux-based data centers. However, as operational complexity scales, traditional rules-based workflows face limitations in managing configuration drift, correlating events, and predicting performance degradation. This has prompted a shift toward integrating artificial intelligence into Ops Center’s telemetry and operational lifecycle. This review explores the application of AI and machine learning techniques to optimize various facets of OEMOC. From predictive asset discovery and patch prioritization to real-time anomaly detection and resource planning, AI offers the potential to transform the platform into a proactive, self-optimizing system. The review evaluates supervised, unsupervised, and reinforcement learning models that can be trained on logs, asset data, and historical events collected across Enterprise Controllers and Agent Controllers. Specific emphasis is placed on using time series forecasting for utilization prediction, clustering techniques for configuration drift detection, and NLP algorithms for intelligent alert triage. Additionally, the review delves into the architectural integration of AI pipelines with OEMOC components, the use of SNMP, syslog, and ITSM APIs for external telemetry fusion, and case studies from financial, government, and telecom deployments. The article also addresses challenges related to model explainability, data governance, and integration within legacy environments. In doing so, it outlines a roadmap for enhancing Ops Center with intelligent automation, turning it from a monitoring tool into a closed-loop operations platform capable of dynamic remediation and resource optimization.
DOI: https://doi.org/10.5281/zenodo.15846496
