# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 6 Datasets and Tools
### Datasets  
To validate and compare different routing algorithms, real-world traffic matrices have been used in the literature [126; 127; 128], for example, the datasets collected in the Abilene, GEANT, and CERNET networks [129], which are publicly available ([[https://github.com/jwwthu/DL4](https://github.com/jwwthu/DL4) Internet/tree/main/TrafficMatrixPrediction/OD_pair]([https://github.com/jwwthu/DL4](https://github.com/jwwthu/DL4) Internet/tree/main/TrafficMatrixPrediction/OD_pair), accessed on 22 September 2024). Each element in a traffic matrix measures the traffic from one node to another. Datasets are collected at different frequencies. The GEANT traffic matrix is sampled every 15 min, and the Abilene and CERNET datasets are sampled every five minutes. More real-world traffic matrices can be obtained using SNDIib [130] ([[http://sndlib.zib.de/home.action](http://sndlib.zib.de/home.action)]([http://sndlib.zib.de/home.action](http://sndlib.zib.de/home.action)), accessed on 22 September 2024). Although real-world traffic matrices have been widely used in the literature, they have some limitations when used for network modeling and routing optimization [131]. First, most existing real-world traffic data are collected in a network topology with a small size, for example, less than 100 nodes, which is not sufficient for evaluating the scalability of new routing algorithms. Second, most existing real-world network traffic matrices were collected many years ago. For example, the Abilene dataset was collected in 2004, the GEANT dataset was collected in 2005, and the CERNET dataset was collected in 2013. However, the Internet has changed significantly since the time when these traffic matrices were collected. These real-world network traffic matrices cannot reflect the latest situations. In addition, it is time and money-consuming to collect traffic measurements in our current complex networks, which makes it difficult to keep the data up-to-date. On the other hand, massive probe information may also affect the normal operation of the Internet. Third, extreme cases, such as network faults and bursty traffic, are rare and difficult to collect in practice. However, these corner cases are essential for evaluating the resilience and recovery capacity of the routing algorithms. To overcome the limitations of real-world traffic measurements, synthetic network traffic matrices are created and leveraged in the evaluation process for routing algorithms based on a given network topology and traffic demand generators. Most existing synthetic network traffic matrices are based on real-world network topologies, which can be found on the website of The Internet Topology Zoo ([[http://www.topology-zoo.org/](http://www.topology-zoo.org/)]([http://www.topology-zoo.org/](http://www.topology-zoo.org/)), accessed on 22 September 2024). For example, synthetic network traffic matrices are created on three real-world network typologies, namely EBONE (Europe), Sprintlink (US), and Tiscali (Europe), all of which are all publicly available [132] ([[https://github.com/yanghu-bit/FlexEntry/tree/main/stagel/data](https://github.com/yanghu-bit/FlexEntry/tree/main/stagel/data)]([https://github.com/yanghu-bit/FlexEntry/tree/main/stagel/data](https://github.com/yanghu-bit/FlexEntry/tree/main/stagel/data)), accessed on 22 September 2024). If the existing network topologies are still not large enough, some graph generators are available for generating massive-scale realistic networks with more than 10,000 nodes and redundancy features, e.g., YARGG (Yet Another Realistic Graph Generator) ([[https://github.com/JroLuttinger/YARGG](https://github.com/JroLuttinger/YARGG)]([https://github.com/JroLuttinger/YARGG](https://github.com/JroLuttinger/YARGG)), accessed on 22 September 2024). The summary of both real-world and synthetic traffic matrices is listed in Table 6.  
Traffic demand generators are used to create synthetic traffic values following pre-defined distributions, for example, gravity and bimodal distributions.