TY - GEN
T1 - A Dynamic Resource Allocation based on Network Traffic Prediction for Sliced Passive Optical Network
AU - Liang, Xuanqiao
AU - Tian, Qinghua
AU - Wang, Fu
AU - Yu, Wensheng
AU - Xin, Xiangjun
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - As the rapid development of the Industries Internet of Things (IIoT), the access network characters with massive connections, deterministic latency and large bandwidth. Therefore, the existing time-division-multiplexing Passive Optical Network (TDM-PON) technology needs to evolve from the original bandwidth scheduling pattern into the cooperative scheduling with multi-network slices (NS). Meanwhile, supporting IIoT and Mobile Fronthaul (MFH) in the TDM-PON via fixed network slicing is challenging, as MFH services may generate microburst traffic, thus impacting the performance of IIoT applications. In view of this, we propose a resource allocation scheme using LSTM neural network to predict traffic in an SDN-based TDM-PON. Our approach considers a network slicing architecture that meets the diverse requirement for both MFH and IIoT services. The results show that our algorithm can reduce the MFH slice latency by 23.3% compared with the typical method (load of 0.8).
AB - As the rapid development of the Industries Internet of Things (IIoT), the access network characters with massive connections, deterministic latency and large bandwidth. Therefore, the existing time-division-multiplexing Passive Optical Network (TDM-PON) technology needs to evolve from the original bandwidth scheduling pattern into the cooperative scheduling with multi-network slices (NS). Meanwhile, supporting IIoT and Mobile Fronthaul (MFH) in the TDM-PON via fixed network slicing is challenging, as MFH services may generate microburst traffic, thus impacting the performance of IIoT applications. In view of this, we propose a resource allocation scheme using LSTM neural network to predict traffic in an SDN-based TDM-PON. Our approach considers a network slicing architecture that meets the diverse requirement for both MFH and IIoT services. The results show that our algorithm can reduce the MFH slice latency by 23.3% compared with the typical method (load of 0.8).
KW - Network Slicing
KW - Network traffic prediction
KW - Resource allocation
KW - TDM-PON
UR - http://www.scopus.com/inward/record.url?scp=85125966044&partnerID=8YFLogxK
U2 - 10.1109/ICOCN53177.2021.9563790
DO - 10.1109/ICOCN53177.2021.9563790
M3 - Conference contribution
AN - SCOPUS:85125966044
T3 - 2021 19th International Conference on Optical Communications and Networks, ICOCN 2021
BT - 2021 19th International Conference on Optical Communications and Networks, ICOCN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Optical Communications and Networks, ICOCN 2021
Y2 - 23 August 2021 through 27 August 2021
ER -