Summary of High Significant Fault Detection in Azure Core Workload Insights, by Pranay Lohia et al.
High Significant Fault Detection in Azure Core Workload Insights
by Pranay Lohia, Laurent Boue, Sharath Rangappa, Vijay Agneeswaran
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel approach to identifying and highlighting high-significance anomalies in Azure Core workload insights. The insights consist of time-series data with varying metric units, which can be affected by faults or anomalies related to metric name, resources region, dimensions, and dimension values. To address this challenge, the authors aim to develop an automated system that can detect a limited number (5-20) of high-significance anomalies per hour, which will have significant user perception and high reconstruction error in any time-series forecasting model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to automatically identify “high significant anomalies” and their associated information for user perception. It focuses on Azure Core workload insights with time-series data featuring different metric units, which can be affected by faults or anomalies related to various factors. The goal is to develop a system that can detect a limited number of high-significance anomalies per hour, making it easier for users to perceive and understand the issues. |
Keywords
» Artificial intelligence » Time series
