Summary of Anomaly Detection in Large-scale Cloud Systems: An Industry Case and Dataset, by Mohammad Saiful Islam and Mohamed Sami Rakha and William Pourmajidi and Janakan Sivaloganathan and John Steinbacher and Andriy Miranskyy
Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset
by Mohammad Saiful Islam, Mohamed Sami Rakha, William Pourmajidi, Janakan Sivaloganathan, John Steinbacher, Andriy Miranskyy
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Software Engineering (cs.SE)
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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 paper addresses the pressing need for effective anomaly detection in Large-Scale Cloud Systems (LCS). Despite the importance of reliable and performant LCS, existing methods struggle due to the lack of large-scale, real-world datasets for benchmarking. The authors aim to bridge this gap by presenting a novel approach to anomaly detection that leverages deep learning techniques. By proposing a new dataset and evaluating various methods using state-of-the-art metrics, the paper contributes to the development of robust and scalable LCS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding problems in big cloud systems before they cause trouble. Right now, it’s hard to test ways to find these problems because there aren’t many real-world examples to work with. The authors want to fix this by creating a new way to detect anomalies using deep learning. They’re also making a large dataset available for others to use and testing different methods to see which ones work best. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning
