Intelligent data flows management for performance improvement of optical label switched network

Modern optical transport networks are currently facing an unprecedented traffic growth driven by rapid development of cloud technologies, Internet of Things and ubiquitous computing. The global data volume doubles every two year, requiring urgent improvement of the transport infrastructure around the world. In this paper, we propose a new algorithm for data Hows management in optical label switched networks. Unlike existing solutions, our algorithm provides the intelligence of scheduling and quality control functionality by using machine learning techniques. Intelligence, introduced to the network, improves the accuracy of scheduling and overall performance. Although, initially our algorithm does not provide the near optimal performance like many other approaches, it is able to improve over time by learning from previous experience.