With the prevalence of some high bandwidth-demanding applications, such as cloud computing, traditional wavelength-division-multiplexing passive optical networks have difficulties in satisfying such growing bandwidth demands due to its limited allocation-flexibility and utilization-efficiency. Therefore, elastic optical networks (EONs). In order to realize the flexibility in EONs, sophisticated routing and spectrum allocation (RSA) algorithms areone of the keyenabling technologies. However, most of the previous RSA algorithms were proposed with invariant routing and spectrum allocation strategies, which ignored considering the time-varying characteristics of EONs due to the variable network architecture and service provisioning. And such time-varying characteristics can deteriorate the spectrum fragmentation and the service blocking performances of EONs, which stimulates the application of various machine-learning technologies in EONs. In this paper, a long short-term memory based routing and spectrum assignment (LSTM-RSA) algorithm is proposed for EONs. By employing the long short-term memory technique to sense the complex status of EONs (e.g. spectral usage on the selected paths), the proposed LSTM-RSA algorithm gradually learns successful strategies through accumulating operation experience in the process of interaction and obtains higher returns through enhanced operation, which helps improve the spectrum fragmentation and the service blocking performances in EONs. Simulation results show that the spectrum fragmentation rate and the blocking rate of the proposed LSTM-RSA algorithm are reduced by about 6% and 8.9%, respectively, when compared to the traditional shortest-path-routing first-fitting RSA algorithm.

随着云计算等高带宽需求应用的普及，传统的波分复用无源光网络由于其有限的分配灵活性和利用效率而难以满足这种不断增长的带宽需求。因此，弹性光网络（EON）。为了实现 EON 的灵活性，复杂的路由和频谱分配 (RSA) 算法是关键技术之一。然而，以往的 RSA 算法大多采用不变的路由和频谱分配策略，由于网络架构和服务提供的可变性而忽略了 EON 的时变特性。而这种时变特性会恶化 EON 的频谱碎片化和服务阻塞性能，从而刺激了各种机器学习技术在 EON 中的应用。在本文中，提出了一种用于 EON 的基于长短期记忆的路由和频谱分配 (LSTM-RSA) 算法。提出的LSTM-RSA算法利用长短期记忆技术感知EONs的复杂状态（例如所选路径上的频谱使用情况），通过在交互过程中积累操作经验，逐步学习成功策略，并通过以下方式获得更高的回报。增强的操作，有助于改善 EON 中的频谱碎片和服务阻塞性能。