Today's optical transport networks are complex already and the support of the new arising services will further increase such complexity. Traditional deterministic network procedures will need to be revisited, especially their operations. Network Operators will require more dynamic approaches to get the best out of their infrastructure. In this context, cognition and machine learning techniques can provide innovative management solutions for traditional telecom operators. In this paper, we explore a dynamic cognitive approach to improve the adaption of Network Operators' operational processes to the new digital age. We propose a dynamic strategy considering the Case-Base Reasoning (CBR) technique for helping to reduce overall costs by optimizing operation margins. In this way, highly competitive exploitation methods to support new services can be deployed. The proposed dynamic algorithms can achieve higher transmitted power efficiency, up to 20% versus previously proposed static solutions, prolonging the transceivers' lifetime and thus addressing telco operator costs reduction.

当今的光传输网络已经很复杂，对新出现的服务的支持将进一步增加这种复杂性。传统的确定性网络程序，特别是其操作，将需要重新审视。网络运营商将需要更多动态方法来充分利用其基础架构。在这种情况下，认知和机器学习技术可以为传统电信运营商提供创新的管理解决方案。在本文中，我们探索了一种动态认知方法，以改善网络运营商的运营流程对新数字时代的适应性。我们提出一种考虑基于案例的推理（CBR）技术的动态策略，以通过优化运营利润来帮助降低总体成本。通过这种方式，可以部署具有高度竞争力的开发方法来支持新服务。与以前提出的静态解决方案相比，提出的动态算法可以实现更高的发射功率效率，最高可达20％，从而延长了收发器的使用寿命，从而解决了电信运营商的成本降低问题。