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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
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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