Traffic Prediction and Resource Allocation Based on Deep Bidirectional LSTM in Data Center Networks | IEEE Conference Publication | IEEE Xplore

Traffic Prediction and Resource Allocation Based on Deep Bidirectional LSTM in Data Center Networks


Abstract:

This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based tr...Show More

Abstract:

This article first proposes an adaptive traffic scheduling strategy for optoelectronic hybrid data centers. The strategy is composed of a deep bidirectional LSTM-based traffic prediction model and a prediction-assisted traffic scheduling method. The simulation results confirm that the presented method can achieve non-congested intra-data center traffic scheduling and higher network performance even under heavy traffic conditions.
Date of Conference: 25-27 June 2021
Date Added to IEEE Xplore: 27 July 2021
ISBN Information:
Conference Location: Nagoya, Japan

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