Deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance strategy in space division multiplexing elastic optical networks.

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Abstract

Space division multiplexing elastic optical networks (SDM-EONs) with multi-core fiber (MCF) are the promising candidate for future optical networks due to their high transmission capacity and high flexibility. However, the inherent defects of inter-core crosstalk and spectrum fragmentation may have some negative impact on the performance of SDM-EONs. A deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance (DLHGA) strategy is proposed in this paper. Firstly, we introduce a deep learning (DL) model to predict future requests, so as to achieve reasonable scheduling of resource in advance. Then, considering the inter-core crosstalk of MCF, we abstract the core, spectrum and time resource as a three-dimensional (3D) model with the hierarchical graphs. Therefore, the resource allocation process is simplified to be mitigating the crosstalk and fragmentation from the perspective of inter-core and intra-core, respectively. Finally, based on DL traffic prediction and different characteristics of hierarchical graph, we present an adaptive resource allocation algorithm considering relieving core adjacency and downgrading modulation format to achieve efficient resource scheduling. We evaluate our DLHGA strategy in different topologies, and the results show that our strategy can efficiently improve network performance in terms to inter-core crosstalk and bandwidth blocking probability compared to earlier approaches.