Simulating Traffic Scenarios with Emphasis on Data Scalability and Portability

In this article, we present a novel simulation platform designed to handle high-traffic scenarios with ease and accuracy. The platform is built upon a combination of basic emulators that work together to provide a comprehensive and systematic approach to simulation. By leveraging these emulators, we can simulate various business scenarios, including data scale and portability challenges.
To tackle the issue of limited data availability, we propose two task challenges: congestion prediction and migration capabilities. These tasks enable users to configure simulation parameters to generate custom datasets, ensuring a strong foundation for model training. Our system-level simulation module also conducts a more in-depth verification of the model’s effectiveness from a network perspective, providing valuable insights for model generalization.
To simplify the complex concepts, imagine building a Lego structure with interconnected pieces. Just as each piece serves a specific purpose in constructing the structure, our emulators work together to create a comprehensive simulation platform. By customizing the dataset and configuring the simulation parameters, we can build a solid foundation for model training, much like adding different colors and shapes to the Lego structure.
In conclusion, our innovative platform offers a robust solution for high-traffic scenarios by combining emulators to provide accurate and systematic simulations. By leveraging these tools, users can overcome data scale and portability challenges, ensuring a more comprehensive understanding of their network’s behavior. With this platform, we aim to streamline the process of model training and validation, ultimately leading to better network performance and efficiency.