Hybrid BSO+BMO: An Optimal Routing of Adaptive Deep Learning Approach with Attention Mechanism for Link Failure Detection in Elastic Optical Network

Abstract

Abstract Because of the enhancement in the data center services, the “Elastic Optical Network (EON)” is a very successive framework to interlink the information centers. The EON can elastically provide a spectrum tailored for multiple needs of bandwidths. In the link failure case, confirming the high stage “Quality of Service (QoS)” for candidate requests after the fault leads to an experiment focus. With the help of the modern digital signal processing approaches and developments in the integrated circuits and the coherent receivers in EON is able to estimate the link failures in the present time. The high-speed network survivability is highly important. When the sizes of the network get enhanced, the likelihood of the node and link impairment is also enhanced. Therefore, to predict the link impairment in EON, an adaptive technique is necessary. To accomplish this objective, a novel methodology is proposed using hybrid heuristic improvement. In the first stage, the required data is gathered and fed into the link failure detection model. The novel method is named an Atrous Spatial Pyramid Pooling – 1 Dimensional Convolution Neural Network with Attention mechanism (ASPP-1DCNN-AM), in which some of the hyper-parameters are tuned by proposing the hybrid algorithm as Iteration-aided Position of Beetle and Barnacles Mating (IPBBM). After forecasting the failure link, the model is in need of finding the optimal routing for better communication. Here, the optimal path is identified by using the IPBBM algorithm. Finally, the validation is done using divergent measurements and in contrast with traditional models. Hence, the designed system demonstrates that it achieves the higher detection results to make the data transmission effectively.