High Significant Fault Detection in Azure Core Workload Insights

Autor:	Lohia, Pranay, Boue, Laurent, Rangappa, Sharath, Agneeswaran, Vijay
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	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 limited number, e.g., 5-20 anomalies reported per hour. The reported anomalies will have significant user perception and high reconstruction error in any time-series forecasting model. Hence, our task is to automatically identify 'high significant anomalies' and their associated information for user perception. Comment: Published in IAAI 2024, which is the Industrial track of AAAI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.09302 Zobrazit plný text záznamu View this record from Arxiv