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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Eun Kyung Lee Viswanathan, H. Pompili, D. |
| Copyright Year | 2013 |
| Abstract | 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. In addition, a Thermal Anomaly-aware Resource Allocation (TARA) scheme is designed to create time-varying thermal fingerprints of the datacenter so to maximize the accuracy and minimize the latency of the aforementioned model-based detection. TARA significantly improves the performance of model-based anomaly detection compared to state-of-the-art resource allocation schemes. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 191 |
| Ending Page | 198 |
| File Size | 1419143 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479902064 |
| e-ISBN | 9780769550411 |
| DOI | 10.1109/DCOSS.2013.8 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Temperature sensors Cooling Heat imbalance Heating Servers Resource management Virtualization Optimization Anomaly detection |
| Content Type | Text |
| Resource Type | Article |
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