arXiv:2510.20640v2 Announce Type: replace Abstract: In this paper, we present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft. We provide insights on the usefulness of this feature as perceived by the cloud service owners and lessons learned from deployment. Specifically, we introduce the problem of recommending the optimal subset of attributes (dimensions) that should be tracked by an automated watchdog (monitor) for cloud services. To begin, we construct the monitor heterogeneous graph at production-scale. The interaction dynamics of
The increasing complexity and scale of cloud systems demand more intelligent and automated monitoring solutions to maintain reliability and performance, driving innovation in AI-driven recommendation systems.
This development enhances the efficiency and effectiveness of cloud infrastructure management, reducing operational costs and improving service stability for large-scale digital operations.
Cloud monitoring shifts from manual configuration to AI-enhanced, automated recommendations for critical attributes, optimizing resource utilization and proactive issue detection.
- · Cloud service providers
- · Enterprises using cloud services
- · AI/ML developers
- · Manual monitoring solution providers
Improved reliability and reduced operational overhead for large cloud deployments due to automated, intelligent monitoring.
Increased adoption of AI-driven tools across various IT operations functions, further automating complex system management.
Potential for a competitive advantage for cloud providers who successfully integrate and scale such intelligent monitoring, attracting more enterprises concerned with uptime and efficiency.
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