Temporal data mining approaches for sustainable chiller management in data centers

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.

[1]  Manish Marwah,et al.  Data Mining for Modeling Chiller Systems in Data Centers , 2010, IDA.

[2]  Tamara Munzner,et al.  LiveRAC: interactive visual exploration of system management time-series data , 2008, CHI.

[3]  Jimeng Sun,et al.  InteMon: continuous mining of sensor data in large-scale self-infrastructures , 2006, OPSR.

[4]  Chandrakant D. Patel,et al.  Application of Exploratory Data Analysis (EDA) Techniques to Temperature Data in a Conventional Data Center , 2007 .

[5]  Manish Marwah,et al.  Sustainable operation and management of data center chillers using temporal data mining , 2009, KDD.

[6]  P. S. Sastry,et al.  Discovering frequent episodes and learning hidden Markov models: a formal connection , 2005, IEEE Transactions on Knowledge and Data Engineering.

[7]  Debprakash Patnaik,et al.  Inferring neuronal network connectivity from spike data: A temporal data mining approach , 2008, Sci. Program..

[8]  Eamonn J. Keogh,et al.  Probabilistic discovery of time series motifs , 2003, KDD '03.

[9]  Philip S. Yu,et al.  Toward Predictive Failure Management for Distributed Stream Processing Systems , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[10]  Tom M. Mitchell,et al.  Continuous hidden process model for time series expression experiments , 2007, ISMB/ECCB.

[11]  Lola Bautista,et al.  Analysis of Environmental Data in Data Centers , 2007 .

[12]  M. Marwah,et al.  Anomalous Thermal Behavior Detection in Data Centers using Hierarchical PCA , 2010 .

[13]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[14]  Eamonn J. Keogh,et al.  Detecting time series motifs under uniform scaling , 2007, KDD '07.

[15]  Hui Xiong,et al.  Failure Prediction in IBM BlueGene/L Event Logs , 2007, ICDM.

[16]  Thomas D. Nielsen,et al.  Alert Systems for Production Plants: A Methodology Based on Conflict Analysis , 2005, ECSQARU.

[17]  Mark Crovella,et al.  Diagnosing network-wide traffic anomalies , 2004, SIGCOMM '04.

[18]  Nir Friedman,et al.  Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.

[19]  Eamonn J. Keogh,et al.  Mining motifs in massive time series databases , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[20]  Jessica Lin,et al.  Finding Motifs in Time Series , 2002, KDD 2002.

[21]  Jimeng Sun,et al.  Streaming Pattern Discovery in Multiple Time-Series , 2005, VLDB.

[22]  T.D. Boucher,et al.  Viability of dynamic cooling control in a data center environment , 2004, The Ninth Intersociety Conference on Thermal and Thermomechanical Phenomena In Electronic Systems (IEEE Cat. No.04CH37543).

[23]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[24]  Chandrakant D. Patel,et al.  On building next generation data centers: energy flow in the information technology stack , 2008, Bangalore Compute Conf..

[25]  Jimeng Sun,et al.  InteMon: intelligent system monitoring on large clusters , 2006, VLDB.

[26]  Kevin P. Murphy,et al.  Modeling changing dependency structure in multivariate time series , 2007, ICML '07.

[27]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[28]  Naren Ramakrishnan,et al.  Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[29]  Lingfeng Shi,et al.  Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series , 2008, Knowledge and Information Systems.

[30]  Michael I. Jordan,et al.  Detecting large-scale system problems by mining console logs , 2009, SOSP '09.

[31]  Armando Fox,et al.  HiLighter: Automatically Building Robust Signatures of Performance Behavior for Small- and Large-Scale Systems , 2008, SysML.

[32]  C. Bash,et al.  Exergy Analysis of Data Center Thermal Management Systems , 2008 .

[33]  Ryen W. White,et al.  Stream prediction using a generative model based on frequent episodes in event sequences , 2008, KDD.

[34]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.