Learning-Based Network Performance Estimators: The Next Frontier for Network Simulation

Over the past few decades, a tremendous amount of research attention has been received to derive the network performance estimation problem. In its context, network performance estimators can provide an early-stage prediction before emulation and real-world deployment, which is essential for network design and optimization. The design philosophy of network performance estimators is to design accurate estimators with scalability and generality. However, conventional rule-based network simulators are not able to satisfy all these demands simultaneously. To achieve these objectives, it has become an inevitable and appealing trend to empower network performance estimators with machine learning, especially with deep learning techniques. In this article, we present a cursory glimpse of existing results over the past five years in learning-based network performance estimators, with a particular focus on understanding the current challenges, the basic ideas and issues of state-of-the-art solutions, and essentially, the open challenges and future directions in research attention.