Journal ArticleDOI
Temporal data mining approaches for sustainable chiller management in data centers
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TLDR
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 are demonstrated.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.read more
Citations
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Probabilistic advisory subsystem as a part of distributed control system of complex industrial process: technical report no. DCSE/TR - 2015 - 01
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Dissertation
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Big Data Techniques For Renewable Energy Market.
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Réseaux et signal : des outils de traitement du signal pour l'analyse des réseaux
TL;DR: In this article, the authors propose nouveaux outils adaptes a l'analyse des reseaux : sociaux, de transport, de neurones, de proteines, of telecommunications.
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