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.

本数据集包含用于测试基于伽马数据集（gamma dataset）训练得到的伽马模型（gamma model）的5000张拓扑图。其拓扑结构通过SNR-BA方法[1]生成，节点在北美尺寸的网格区域内均匀随机分布，且节点间最小间距为100公里。吞吐量标签通过以下方式计算：采用首次适配k最短路径算法实现无阻塞的路由与波长分配最大化，并结合高斯噪声模型模拟物理层损伤。
[1] R. Matzner、D. Semrau、R. Luo、G. Zervas及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）》，《光通信网络期刊（JOCN）》，2021年8月，第13卷第8期，第D53–D67页，DOI: 10.1364/JOCN.423490。