High Significant Fault Detection in Azure Core Workload Insights

arXiv:2404.09302v1 Announce Type: new
Abstract: Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value associated with the data. For Azure Core, an important task is to highlight faults or anomalies to the user on a dashboard that they can perceive easily. The number of anomalies reported should be highly significant and in a …

abstract arxiv azure core cs.ai cs.dc cs.lg data detection dimensions insights resources series type units value