Improvement of the Prediction-Based Energy Efficient Ethernet Strategy

• Published in: IEEE access Vol. 7; pp. 156420 - 156429
• Main Authors: Segolene, Numukobwa, Liao, Kaiqin, Jiang, Wanchun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Autoregressive models; Computational modeling; Data communication; Data models; Data transmission; Energy consumption; Energy efficient ethernet; Ethernet; LSTM; Microsoft Windows; Model accuracy; Power consumption; Power demand; prediction; Predictive models; Strategy; Traffic information; Traffic models; Windows (intervals)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2948840

Ethernet consumes maximum energy even when there is no data transmission. To reduce the power consumption, IEEE 802.3az standardizes the Energy Efficient Ethernet that enhances Ethernet with the low power idle state without data transmission. However, this standard does not describe the specific strategy about when the Ethernet link will enter or exit the low power idle state. Recently, they proposed the EEEP strategy for the 1-10Gbps EEE to reduce power consumption. Specifically, EEEP predicts the future traffic in a time window by the Autoregressive Integrated Moving Average (ARIMA) model and determines when to enter or exit the low power idle state according to the prediction results. However, the EEEP strategy relies on the prediction accuracy of the ARIMA model for good energy saving. This paper proposes to use the Long Short Term Memory (LSTM) model for EEEP to improve the prediction accuracy. Owning to the historic traffic information, the LSTM model can achieve about 11% improvement on the accuracy compared to ARIMA, and thus helps EEEP to achieve better energy saving, according to our trace-driven simulation results.