Traffic Scheduling based on Spiking Neural Network in Hybrid E/O Switching Intra-Datacenter Networks

With the emergence of cloud computing and several ultra-high bitrate data center applications, hybrid E/O switching intra-datacenter network (HS-IDCN) has become an integral architecture of current and future data centers. To meet the diverse and heterogeneous performance requirements of HS-IDCNs, people have considered traffic prediction as a promising solution to ensure effective and flexible traffic scheduling. However, the low accuracy of existing deep learning-based prediction approaches, which cannot fully extract the features of burst traffic, directly restricts the efficiency of traffic scheduling. In view of this, this study considers the spiking neural networks that can predict high burstiness and heterogeneous traffic to further improve the efficiency of traffic scheduling. We first propose a supervised spiking neural network (s-SNN) framework for high accuracy traffic prediction in HS-IDCNs. A traffic prediction-based traffic scheduling (TP-TS) algorithm for HS-IDCNs is then introduced by considering the prediction results of s-SNN. The s-SNN framework can enhance the extraction ability of burst traffic features in a supervised fashion by mimicking the multi-synaptic mechanism of biological neuron system. The efficiency and feasibility of s-SNN are verified on the brain model simulator. The performance of TP-TS is also evaluated in terms of resource utilization and path blocking probability, compared with other scheduling schemes.