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RACNet: a high-fidelity data center sensing network
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Conference On Embedded Networked Sensor Systems archive
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems table of contents
Berkeley, California
SESSION: Data collection table of contents
Pages: 15-28  
Year of Publication: 2009
ISBN:978-1-60558-519-2
Authors
Chieh-Jan Mike Liang  Johns Hopkins University
Jie Liu  Microsoft Research
Liqian Luo  Google
Andreas Terzis  Johns Hopkins University
Feng Zhao  Microsoft Research
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

RACNet is a sensor network for monitoring a data center's environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data center's safety and energy efficiency. RACNet overcomes the network's large scale and density and the data center's harsh RF environment to achieve data yields of 99% or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Chieh-Jan Mike Liang: colleagues
Jie Liu: colleagues
Liqian Luo: colleagues
Andreas Terzis: colleagues
Feng Zhao: colleagues