Telemetry System for Data Center Zone Anomaly Detection

The patent segments the data center into multiple zones based on geographic location and groups IHSs within each zone. By analyzing metric data at the zone level rather than individually for each IHS, the system identifies location-specific anomalies more efficiently. This segmentation reduces the complexity of analyzing data from all IHSs uniformly while improving the precision of detecting spatial patterns in temperature, power, and network metrics.

The patent applies local quality by treating each zone as having unique characteristics that require customized analysis. Instead of applying uniform monitoring thresholds across the entire data center, the system identifies anomalies relative to neighboring zones, allowing each zone to be evaluated based on its local environmental and operational context. This improves anomaly detection precision by accounting for location-specific conditions.

The patent implements local quality by enabling zone-specific environmental control based on detected anomalies. When the system identifies that a particular zone has temperature deviations or other metric anomalies, it can apply targeted cooling or operational adjustments only to that zone rather than uniformly across the entire data center. This maintains equipment reliability by addressing local issues while reducing unnecessary energy consumption in zones that are operating normally.

The patent applies partial action by providing cooling and resource allocation adjustments only to the extent necessary for zones with detected anomalies. Instead of continuously over-provisioning resources to maintain uniform conditions across all zones, the system intervenes partially and selectively in zones that require it, thereby reducing overall energy loss while maintaining adequate reliability where needed.

The patent reduces zone management complexity through segmentation by automatically grouping IHSs into zones based on their geographic locations and analyzing their metric data collectively. This automated segmentation eliminates the need for manual zone creation and management, allowing the system to implement targeted adjustments while keeping management complexity manageable. The segmentation approach enables energy-efficient zone-specific control without requiring complex manual intervention.

The patent applies self-service by enabling the telemetry system to automatically identify zones, detect anomalies, and trigger appropriate adjustments without requiring extensive manual configuration or management. The system autonomously performs zone identification based on IHS locations, analyzes metric data to detect patterns, and initiates targeted responses, thereby reducing the operational complexity of managing multiple zones while achieving energy efficiency benefits.

The patent reduces response time through segmentation by grouping IHSs into zones and analyzing their metric data collectively to identify anomalies. Instead of requiring manual investigation of each individual IHS, the system detects zone-level patterns in temperature, power, and network metrics that indicate problems affecting multiple systems. This segmentation approach maintains diagnostic precision by identifying the affected zone while dramatically reducing the time required to detect and respond to anomalies compared to individual IHS analysis.

The patent applies merging by combining the metric data from multiple IHSs within a zone to detect anomalies that may affect multiple systems simultaneously. By merging the analysis of temperature, power, and network metrics across grouped IHSs, the system achieves comprehensive diagnostics at the zone level, identifying patterns that would be difficult to detect through individual IHS investigation while reducing overall response time through parallel processing of grouped data.