The flexible resource management in optical data center networks based on machine learning and SDON

Abstract

Based on software defined optical network and machine learning, the flexible resource management mechanism (ML-FRM) is proposed, which meets the resource requirements of different services in the optical data center networks. The machine learning is integrated to the SDON controller, which accomplishes the resource allocation algorithms according to the classification and clustering results. ML-FRM firstly utilizes unsupervised learning K-means algorithm to cluster traffic flows, and uses supervised learning support vector machine (SVM) algorithm to realize hierarchical classification of channel qualities. Fragmentation-Function-Fit algorithm is proposed to reduce the blocking probability, the results show that it has the lower blocking probability than First-Fit and Exact-First-Fit algorithms. ML-FRM allocates the required resources through different algorithms based on different traffic flow clustering results, and uses different modulation methods for different channel qualities. The analysis results show that ML-FRM has lower blocking probability, acceptable complexity level, and higher spectrum resource utilization efficiency than other algorithms under different offered load level.