Authors: Dr. Kevin Brooks, Laura Mitchell, Dr. Daniel Foster, Christopher Evans, Dr. Olivia Bennett, Jeji Krishnan
Abstract: Large-scale messaging systems serve as the backbone of enterprise communication, supporting millions of users and high volumes of real-time interactions, but their growing complexity presents significant challenges for escalation support teams tasked with rapid issue diagnosis during outages and performance degradations. Traditional troubleshooting methods rely heavily on manual analysis of thread dumps, mailbox logs, and distributed system metrics, which is time-consuming, error-prone, and inefficient under critical conditions. This paper proposes a dashboard-driven operational intelligence framework that transforms escalation support by integrating diverse data sources into a unified, interactive platform offering real-time visibility into system behavior. By leveraging advanced analytics, automated event correlation, and visual representations such as graphs and heatmaps, the framework enables faster detection of anomalies, bottlenecks, and failure patterns. The system introduces intelligent data aggregation and one-click diagnostic capabilities that significantly reduce the effort required for root cause analysis while enhancing accuracy. Additionally, predictive insights derived from historical patterns support proactive issue resolution, minimizing system downtime. Experimental evaluation demonstrates substantial improvements in mean time to resolution (MTTR), diagnostic precision, and overall operational efficiency compared to conventional approaches. The results emphasize the effectiveness of combining operational intelligence with visual analytics to enhance the reliability, scalability, and performance of large-scale messaging systems, providing a practical foundation for next-generation escalation engineering and intelligent observability solutions.
DOI: http://doi.org/10.5281/zenodo.20156995
