Carnegie Mellon School of Computer Science Forecasting with Cyber-physical Interactions in Data Centers Lei Li PDL Seminar 9/28/2011.

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Presentation transcript:

Carnegie Mellon School of Computer Science Forecasting with Cyber-physical Interactions in Data Centers Lei Li PDL Seminar 9/28/2011

Outline Overview of time series mining –Time series examples –What problems do we solve Motivation Experimental setup ThermoCast: the forecasting model Results Other time series models and algorithms 2(c) Lei Li 2012

What is co-evolving time series? 3 Correlated multidimensional time sequences with joint temporal dynamics (c) Lei Li 2012

Goal: generate natural human motion –Game ($57B) –Movie industry Challenge: –Missing values –“naturalness” 4 Motion Capture Right hand Left hand walking motion [Li et al 2008a] (c) Lei Li 2012

Environmental Monitoring Problem: early detection of leakage & pollution Challenge: noise & large data 5 Chlorine level in drinking water systems [Li et al 2009] (c) Lei Li 2012

Network Security Challenge: Anomaly detection in computer network & online activity 6 BGP # updates on backbone from Webclick for news from NTT Webclick for TV (c) Lei Li 2012

Time Series Mining Problems Forecasting Imputation (missing values) Compression Segmentation, change/anomaly detection Clustering Similarity queries Scalable/Parallel/Distributed algorithms 7 See my thesis for algorithms covering these problems (c) Lei Li 2012

Outline Overview of time series mining –Time series examples –What problems do we solve Motivation Experimental setup ThermoCast: the forecasting model Results Other time series models and algorithms 8(c) Lei Li 2012

Datacenter Monitoring & Management Temperature in datacenter Goal: save energy in data centers –US alone, $7.4B power consumption (2011) Challenge: –Huge data (1TB per day) –Complex cyber physical systems 9(c) Lei Li 2012

Typical Data Center Energy Consumption LBL data center Google data center [Barroso 09] [LBNL/PUB-945] 10(c) Lei Li 2012

Towards Thermal Aware DC Management Data centers are often over provisioned, with ≈40% of energy spent for cooling (total=$7.4B) How can we improve energy efficiency in modern multi-MegaWatt data centers? 11 JHU data center with Genomote (c) Lei Li 2012

Air cycle in DC 12(c) Lei Li 2012