Name
Abstract | ||
|---|---|---|
We present a data mining approach to model the cooling infrastructure in data centers, particularly the chiller ensemble. These infrastructures are poorly understood due to the lack of “first principles” models of chiller systems. At the same time, they abound in data due to instrumentation by modern sensor networks. We present a multi-level framework to transduce sensor streams into an actionable dynamic Bayesian network model of the system. This network is then used to explain observed system transitions and aid in diagnostics and prediction. We showcase experimental results using a HP data center in Bangalore, India. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13062-5_13 | IDA |
Keywords | Field | DocType |
data mining approach,data center,chiller system,chiller ensemble,hp data center,observed system transition,sensor stream,modern sensor network,cooling infrastructure,actionable dynamic bayesian network,dynamic bayesian network,first principle,sensor network,data mining | Data mining,Computer science,Chiller,Bayesian network,Data center,Wireless sensor network,Dynamic Bayesian network | Conference |
Volume | ISSN | ISBN |
6065 | 0302-9743 | 3-642-13061-5 |
Citations | PageRank | References |
6 | 0.51 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debprakash Patnaik | 1 | 191 | 14.89 |
| Manish Marwah | 2 | 672 | 50.11 |
| Ratnesh K. Sharma | 3 | 483 | 53.37 |
| Naren Ramakrishnan | 4 | 1913 | 176.25 |