High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can help to solve this issue by realizing a network that can observe, act, learn and optimize its performance, taking into account end-to-end goals.In this letter we present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We focus on the approaches developed in the project for optical performance monitoring, which enable the feedback from the physical layer to the cognitive decision system by providing accurate description of the performance of the established lightpaths. Keywords: optical networks, cognition, dynamic optical networks, optical performance monitoring.
INTRODUCTIONOptical networks are nowadays becoming more heterogeneous, ranging from different types of services, switching paradigms to physical interfaces. Therefore, network operators are facing the challenge of supporting a plethora of services, each with individual requirements on quality of service (QoS). Operators have also available different transmission technologies for their optical transport networks, such as coding, modulation formats or data rates. Moreover, in the short and medium term, optical networks may simultaneously support different switching paradigms such as semi-static and dynamic circuit switching. Hence, a key issue of highly heterogeneous networks is how to efficiently control and manage network resources while fulfilling user demands and complying with QoS requirements. A solution for such a scenario may come from cognitive networks. A cognitive network is defined as "a network with a process that can perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals" [1]. Hence, a cognitive network should provide better end-to-end performance than a non-cognitive network. Cognitive paradigm have already shown to be a promising solution for wireless networks [1,2]. Cognition is also applicable to optical communication architectures [3][4][5], since it can offer flexibility to telecom operators by optimizing simultaneously physical layer components' characteristics (modulation format, forward error correction -FEC, wavelength capacity, etc.) and network layer parameters (bandwidth, number of simultaneous lightpaths, QoS, etc.) depending on application or service requirements.The aim of CHRON is to develop a showcase network architecture and a control plane that efficiently uses resources in a heterogeneous scenario, while fulfilling the QoS requirements of each type of services. To achieve this goal, CHRON relies on cognition, so that control decisions mus...