Model-based Thermal Anomaly Detection in Cloud Datacenters

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Cloud
Format: PDF
The growing importance, large scale, and high server density of high-performance computing datacenters make them prone to strategic attacks, misconfigurations, and failures (cooling as well as computing infrastructure). Such unexpected events lead to thermal anomalies - hotspots, fugues, and coldspots - which significantly impact the total cost of operation of datacenters. A model-based thermal anomaly detection mechanism, which compares expected (obtained using heat generation and extraction models) and observed thermal maps (obtained using thermal cameras) of datacenters is proposed.
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