Gamma Test Dataset

This set of data houses the 5000 graphs used for testing the gamma model that has been trained on the gamma dataset. It houses topologies generated via SNR-BA [1] with nodes scattered uniformly randomly with a minimum radius of 100km between them over a grid the size of north america. The throughput labels are calculated via maximising the routing and wavelength assignment with zero blocking using first-fit k-shortest-paths and implementing the physical layer impairments using the gaussian noise model. [1] R. Matzner, D. Semrau, R. Luo, G. Zervas, and P. Bayvel, ‘Making intelligent topology design choices: understanding structural and physical property performance implications in optical networks [Invited]’, J. Opt. Commun. Netw., JOCN, vol. 13, no. 8, pp. D53–D67, Aug. 2021, doi: 10.1364/JOCN.423490.

本数据集包含5000张图，用于测试基于伽马数据集（gamma dataset）训练得到的伽马模型（gamma model）。该数据集包含通过SNR-BA方法生成的拓扑结构：节点在北美尺寸的网格范围内均匀随机分布，节点间最小间距为100km。吞吐量标签通过以下流程计算得到：采用首次适配k最短路径算法实现零阻塞的路由与波长分配优化，并结合高斯噪声模型（Gaussian noise model）模拟物理层损伤。[1] R. Matzner、D. Semrau、R. Luo、G. Zervas与P. Bayvel，《智能拓扑设计选择：理解光网络中结构与物理属性对性能的影响 [特邀论文]》，J. Opt. Commun. Netw., JOCN，第13卷第8期，第D53–D67页，2021年8月，DOI: 10.1364/JOCN.423490。