TY - GEN
T1 - Kalman Filter-based Heavy Hadoop Job Detection Method for Energy Efficient Hybrid Electro-Optical Intra-Data Center Networks
AU - Murakami, Masaki
AU - Dubrana, Nicolas
AU - Uematsu, Yoshihiko
AU - Okamoto, Satoru
AU - Yamanaka, Naoaki
N1 - Funding Information:
This work is supported by “HOLST (High-speed Optical Layer 1 Switch system for Time slot switching optical data center networks) Project” funded by NEDO of Japan
Publisher Copyright:
© 2021 The Author(s)
PY - 2021
Y1 - 2021
N2 - This paper proposes Hadoop job detection method using the Kalman filter and network configuration procedure for hybrid electro-optical intra-data center networks. The simulation results show improvement of detection accuracy and energy saving effect.
AB - This paper proposes Hadoop job detection method using the Kalman filter and network configuration procedure for hybrid electro-optical intra-data center networks. The simulation results show improvement of detection accuracy and energy saving effect.
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M3 - Conference contribution
AN - SCOPUS:85139073774
T3 - 2021 Opto-Electronics and Communications Conference, OECC 2021
BT - 2021 Opto-Electronics and Communications Conference, OECC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Opto-Electronics and Communications Conference, OECC 2021
Y2 - 3 July 2021 through 7 July 2021
ER -