Anomaly monitoring in high-density data centers based on gaussian distribution anomaly detection algorithm
| Autor: | Hengmao Pang, Zhu Mei, Jun Yu, Haiyang Chen, Lin Wang, Lu Shida, Mingjie Xu, Lin Qian, Lingpeng Shi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Risk analysis
Distribution (number theory) business.industry Computer science Gaussian Anomaly detection algorithm High density 020206 networking & telecommunications 02 engineering and technology computer.software_genre symbols.namesake Material resources 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Data center Data mining Anomaly (physics) business computer |
| Zdroj: | 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA). |
| DOI: | 10.1109/aeeca49918.2020.9213549 |
| Popis: | Anomaly monitoring has always been an important issue for data center operations and maintenance. Traditional data centers use human and physical sensors to conduct inspections and risk analysis, which consumes a lot of manpower and material resources. Therefore, based on the Anomaly Detection algorithm based on Gaussian distribution, this paper skillfully proposes an anomaly detection algorithm Hddcad suitable for high-density data centers, which greatly improves the efficiency of anomaly monitoring in the data center and reduces the data center. Management costs in high-density designs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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