Research Article
Mobile Visual Analytics for Datacenter Power and Cooling Management
@INPROCEEDINGS{10.1007/978-3-642-12607-9_11, author={Ratnesh Sharma and Ming Hao and Ravigopal Vennelakanti and Manish Gupta and Umeshwar Dayal and Cullen Bash and Chandrakant Patel and Deepa Naik and A. Jayakumar and Sairabanu Ganihar and Ramesh Munusamy and Vani Mohan}, title={Mobile Visual Analytics for Datacenter Power and Cooling Management}, proceedings={Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers}, proceedings_a={MOBICASE}, year={2012}, month={10}, keywords={Visual Analytics Sensors Data center Thermal Management Data mining}, doi={10.1007/978-3-642-12607-9_11} }- Ratnesh Sharma
Ming Hao
Ravigopal Vennelakanti
Manish Gupta
Umeshwar Dayal
Cullen Bash
Chandrakant Patel
Deepa Naik
A. Jayakumar
Sairabanu Ganihar
Ramesh Munusamy
Vani Mohan
Year: 2012
Mobile Visual Analytics for Datacenter Power and Cooling Management
MOBICASE
Springer
DOI: 10.1007/978-3-642-12607-9_11
Abstract
The demand for data center solutions with lower total cost of ownership and lower complexity of management is driving the creation of next generation datacenters. The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive “always-on” data center infrastructure, reduction in the recurring cost of power and management of physical resources. In this paper, we describe a tool that allows the data center facility managers and administrators to view and analyze the Key Performance Indicators (KPIs) associated with their data centers using pixel cell-based [10,11] visual analytics. The basic idea of our technique is to use the smallest element in the display to present the detailed information of the poser and thermal data records. Administrators can quickly recognize the patterns, trends, and anomalies. Furthermore, we discuss case studies of mobile visual analytics for energy and thermal state monitoring utilizing data from a rich sensor network.
