Naive-LSTM based services awareness of edge computing elastic optical networks

Abstract

Great challenges and demands are presented by increasing edge computing services for current elastic optical networks (EONs) to deal with serious diversity and complexity of these services. To improve the match degree between edge computing and optical network, the services awareness function is necessary for EON. This article proposes a Naive long short-term memory (Naive-LSTM) based services awareness strategy of the EON, where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the LSTM model. Moreover, the proposed algorithm can generate the probability output result to determine the quality of service (QoS) policy of EONs. After well learning operation, these Naive-LSTM classification agents in edge nodes of EONs are able to perform services awareness by obtaining data traffic characteristics from services traffics. Test results show that the proposed approach is feasible and efficient to improve edge computing ability of EONs.

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