Name
Abstract | ||
|---|---|---|
The article describes our work with the deployment of a 600-piece temperature sensor network, data harvesting framework, and real time analysis system in a Data Center (hereinafter DC) at the Johns Hopkins University. Sensor data streams were processed by robust incremental PCA and K-means clustering algorithms to identify outlier and changepoint events. The output of the signal processing system allows us to better understand the temperature patterns of the DataCenter's inner space and make possible the online detection of unusual transient and changepoint events, thus preventing hardware breakdown, optimizing the temperature control efficiency, and monitoring hardware workloads. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/HPEC.2014.7040959 | High Performance Extreme Computing Conference |
Keywords | Field | DocType |
computer centres,pattern clustering,principal component analysis,real-time systems,sensor fusion,DC,change point detection,data center,data harvesting framework,incremental PCA,k-means clustering algorithm,real time analysis system,sensor data stream,temperature sensor network | Data mining,Signal processing,Data stream mining,Change detection,Computer science,Temperature control,Outlier,Real-time computing,Cluster analysis,Data center,Wireless sensor network | Conference |
ISSN | ISBN | Citations |
2377-6943 | 978-1-4799-6232-7 | 1 |
PageRank | References | Authors |
0.38 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry Mishin | 1 | 9 | 3.35 |
| Kieran Brantner-Magee | 2 | 1 | 0.38 |
| Ferenc Czako | 3 | 1 | 0.38 |
| Alexander S. Szalay | 4 | 1 | 0.38 |