Summary of Cluster-wide Task Slowdown Detection in Cloud System, by Feiyi Chen et al.
Cluster-Wide Task Slowdown Detection in Cloud System
by Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach for detecting slow task detection in cloud computing clusters is presented. The paper addresses the limitations of traditional anomaly detection methods, which focus on single-task analysis, by introducing a cluster-wide slowdown detection method. This method leverages the duration time distribution of tasks across a cluster to identify potential issues, reducing computation complexity regardless of the number of concurrent tasks. The paper’s contributions include a new statistical model for task slowdown detection and an evaluation framework that considers various cloud computing scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cloud computing is used by many people every day. Sometimes, certain tasks can take longer than expected to complete. This can affect how well the cloud works and even cause problems if not addressed quickly. A team of researchers looked at this issue and came up with a new way to detect when tasks are taking too long. Instead of just looking at one task, they considered many tasks happening at the same time in large cloud computing clusters. This approach helps reduce the amount of work needed to identify slow tasks and can improve overall cloud performance. |
Keywords
* Artificial intelligence * Anomaly detection * Statistical model
