T*GreenHDFS: A Cyber-Physical, Data-Centric Cooling Energy Costs Reduction Approach for Big Data Analytics Cloud

Kaushik, Rini T.

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https://hdl.handle.net/2142/30404 Copy

Description

Title

T*GreenHDFS: A Cyber-Physical, Data-Centric Cooling Energy Costs Reduction Approach for Big Data Analytics Cloud

Author(s)

Kaushik, Rini T.

Contributor(s)

• Nahrstedt, Klara
• Abdelzaher, Tarek F.

Issue Date

2011-05

Keyword(s)

• Energy
• hadoop
• data-intensive computing
• HDFS
• cooling
• Flovent
• Thermal-Management
• Cooling Energy Costs
• Thermal-Aware Data Placement
• Data center
• cluster

Abstract

Big Data explosion and surge in large-scale Big Data analytics cloud infrastructure have led to burgeoning energy costs and present a challenge to the existing run-time cooling energy management techniques. T*GreenHDFS, a thermal-aware cloud file system, takes a novel, data-centric approach to reduce cooling energy costs. On the physical-side, T*GreenHDFS is cognizant of the uneven thermal-profile in the data centers due to complex airflow patterns, varying ability of the cooling system to cool different parts of the data center, and run-time load distribution. On the cyber-side, T*GreenHDFS is aware of the differences in the data-semantics of the data placed on the clusters. Based on this knowledge, and coupled with its predictive data models, T*GreenHDFS does proactive, thermal-aware data placement, which implicitly results in thermal-aware computation placement in the Big Data analytics cloud compute model. Evaluation results show up to 59% reduction in the cooling energy costs with T*GreenHDFS.

Type of Resource

• text
• other

Language

en

Permalink

http://hdl.handle.net/2142/30404

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