Elastic optical networks emerge as a reliable backbone platform covering the next‐generation connectivity requirements. It consists of advanced enabling components that provide the ability for extensive configuration leading to performance improvement in many areas of interest. Higher layer analytics like data from IP traffic prediction can assist in the process of allocating resources at the optical layer. This way, light connections are established more efficiently while targeting specific performance goals. For that purpose, an algorithm is designed and evaluated that exploits traffic prediction of data transfers between nodes of an optical metro or backbone network. Next, it utilizes adaptive functionality based on particle swarm optimization to find paths with available spectrum resources. These resources can facilitate more efficiently the future traffic demand, since traffic prediction data are considered when finding the related paths. The innovative resource allocation method is evaluated using small and very large real topologies. It scales (in execution time and resource usage) according to node increase, executes in feasible time frames, and reduces transponder utilization resulting to increased energy efficiency.

中文翻译：

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利用群体智能利用IP层流量预测分析来分配频谱资源

弹性光网络已成为满足下一代连接需求的可靠骨干平台。它由先进的使能组件组成，这些组件提供了进行广泛配置的能力，从而可以改善许多关注领域的性能。更高层的分析（如来自IP流量预测的数据）可以帮助在光学层分配资源的过程。这样，可以在针对特定性能目标的同时更有效地建立光连接。为此，设计并评估了一种算法，该算法利用了光学城域网或骨干网节点之间的数据传输流量预测。接下来，它利用基于粒子群优化的自适应功能来查找具有可用频谱资源的路径。这些资源可以更有效地促进将来的流量需求，因为在查找相关路径时会考虑流量预测数据。使用小的和非常大的实际拓扑对创新的资源分配方法进行评估。它根据节点的增加进行缩放（在执行时间和资源使用方面），在可行的时间范围内执行，并降低转发器的利用率，从而提高能源效率。