document: Sustainable operation and management of data center chillers using temporal data mining Motivation : Data centers are a critical component of modern IT infrastructure but are also among the worst environmental offenders through their increasing energy usage and the resulting large carbon footprints . Efficient management of data centers , including power management , networking , and cooling infrastructure , is hence crucial to sustainability . In the absence of a ` first-principles ' approach to manage these complex components and their interactions , data-driven approaches have become attractive and tenable . Results : We present a temporal data mining solution to model and optimize performance of data center chillers , a key component of the cooling infrastructure . It helps bridge raw , numeric , time-series information from sensor streams toward higher level characterizations of chiller behavior , suitable for a data center engineer . To aid in this transduction , temporal data streams are first encoded into a symbolic representation , next run-length encoded segments are mined to form frequent motifs in time series , and finally these metrics are evaluated by their contributions to sustainability . A key innovation in our application is the ability to intersperse `` do n't care '' transitions ( e.g. , transients ) in continuous-valued time series data , an advantage we inherit by the application of frequent episode mining to symbolized representations of numeric time series . Our approach provides both qualitative and quantitative characterizations of the sensor streams to the data center engineer , to aid him in tuning chiller operating characteristics . This system is currently being prototyped for a data center managed by HP and experimental results from this application reveal the promise of our approach .
doc_bio_tags: O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O