TY - GEN
T1 - A failure-tolerant and spectrum-efficient wireless data center network design for improving performance of big data mining
AU - Suto, Katsuya
AU - Nishiyama, Hiroki
AU - Kato, Nei
AU - Nakachi, Takayuki
AU - Sakano, Toshikazu
AU - Takahara, Atsushi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - Wireless Data Center Network (Wi-DCN) is considered one of the most promising future data center architectures due to its low installation and management cost and high flexibility of network design. However, the existing Wi-DCN is, still, not capable of providing an efficient big data mining service such as MapReduce because its topology (i.e., Cayley graph with same degree) cannot achieve enough connectivity on the breakdown of servers and spectrum efficiency, which are important factors to improve the performance of big data mining. Therefore, in order to modify the existing Wi-DCN for big data mining, this paper proposes a spherical rack architecture based on a bimodal degree distribution that improves both failure tolerance and spectrum efficiency. Extensive computer simulations demonstrate the effectiveness of our proposed rack architecture in terms of data transmission time required for MapReduce under a failure-prone environment.
AB - Wireless Data Center Network (Wi-DCN) is considered one of the most promising future data center architectures due to its low installation and management cost and high flexibility of network design. However, the existing Wi-DCN is, still, not capable of providing an efficient big data mining service such as MapReduce because its topology (i.e., Cayley graph with same degree) cannot achieve enough connectivity on the breakdown of servers and spectrum efficiency, which are important factors to improve the performance of big data mining. Therefore, in order to modify the existing Wi-DCN for big data mining, this paper proposes a spherical rack architecture based on a bimodal degree distribution that improves both failure tolerance and spectrum efficiency. Extensive computer simulations demonstrate the effectiveness of our proposed rack architecture in terms of data transmission time required for MapReduce under a failure-prone environment.
KW - Degree distribution
KW - MapReduce performance
KW - Rack design
KW - Wireless data center network
UR - http://www.scopus.com/inward/record.url?scp=84940421750&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940421750&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2015.7145601
DO - 10.1109/VTCSpring.2015.7145601
M3 - Conference contribution
AN - SCOPUS:84940421750
T3 - IEEE Vehicular Technology Conference
BT - 2015 IEEE 81st Vehicular Technology Conference, VTC Spring 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 81st IEEE Vehicular Technology Conference, VTC Spring 2015
Y2 - 11 May 2015 through 14 May 2015
ER -