Name
Abstract | ||
|---|---|---|
Practically every large IT organization hosts data centers---a mix of computing elements, storage systems, networking, power, and cooling infrastructure---operated either in-house or outsourced to major vendors. A significant element of modern data centers is their cooling infrastructure, whose efficient and sustainable operation is a key ingredient to the “always-on” capability of data centers. We describe the design and implementation of CAMAS (Chiller Advisory and MAnagement System), a temporal data mining solution to mine and manage chiller installations. CAMAS embodies a set of algorithms for processing multivariate time-series data and characterizes sustainability measures of the patterns mined. We demonstrate three key ingredients of CAMAS---motif mining, association analysis, and dynamic Bayesian network inference---that help bridge the gap between low-level, raw, sensor streams, and the high-level operating regions and features needed for an operator to efficiently manage the data center. The effectiveness of CAMAS is demonstrated by its application to a real-life production data center managed by HP. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1989734.1989738 | ACM TIST |
Keywords | DocType | Volume |
temporal data mining approach,key ingredient,management system,data center,motif mining,multivariate time-series data,modern data center,chiller advisory,real-life production data,cooling infrastructure,temporal data mining solution,sustainable chiller management,data centers,measurement,management,algorithms,chillers,clustering | Journal | 2 |
Issue | ISSN | Citations |
4 | 2157-6904 | 8 |
PageRank | References | Authors |
0.84 | 27 | 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 |