# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 4 Open Challenges and Opportunities
### Congestion Control with Large Language Models  
Historically, congestion control in network systems has relied heavily on mathematical models to manage data traffic and prevent network overloads [182; 183; 184; 185; 186]. These models provide an initial approximation of the actual network behavior, which can be exceedingly complex to analyze directly. Over time, several sophisticated and effective models, such as queuing theory and stochastic processes, have been developed. For network engineers, selecting the appropriate congestion control model often involves balancing mathematical simplicity with the fidelity of the model to real-world network dynamics. As network systems have become increasingly complex, identifying and applying the right model has become more critical. One of the most impactful approaches to congestion control is the Additive-Increase/Multiplicative-Decrease (AIMD) algorithm [187]. AIMD works by gradually increasing the data transmission rate until packet loss occurs, which signals network congestion. Upon detecting packet loss, the algorithm significantly reduces the transmission rate to alleviate congestion. This cycle of gradually increasing and then sharply decreasing the transmission rate helps maintain a balance between network utilization and congestion, making AIMD a cornerstone in traditional congestion control methods. The ingenuity behind the AIMD algorithm can be appreciated through a classical illustration of two flows, as shown in Figure 4a. This visual representation of the AIMD algorithm demonstrates the convergence of the rates of two users to the fairness line [187; 188]. When the rate iterations are below the efficiency line, the additive-increase mechanism appears as a 45-degree increment plot. Above the efficiency line, the multiplicative-decrease mechanism appears as a slide to the midpoint between the current rate iteration and the origin. The AIMD algorithm, foundational to TCP congestion control [189; 190], not only plays a critical role in managing traffic flow but also connects deeply to optimization theory through the Perron-Frobenius theorem and eigenvector computation. In distributed systems, where resources like bandwidth are shared, the AIMD algorithm ensures fairness by balancing the data transmission rate among nodes. The Perron-Frobenius theorem, which deals with the spectral properties of non-negative matrices, provides a mathematical  
Figure 4: Iterative balancing of resource allocations using AIMD. (**a**) A visual representation of the AIMD algorithm. (**b**) An illustration of the convergence of AIMD resource allocation based on Perron-Frobenius theory. The bold arrow represents the Perron-Frobenius right eigenvector of a non-negative matrix, illustrating its convergence towards the fairness line. framework for understanding how distributed systems converge to stable states [191]. Specifically, the dominant eigenvector characterizes the long-term behavior of such systems, including network congestion control. This connection helps explain why iterative adjustments of AIMD lead to fairness in resource allocation, as the system naturally converges to an equilibrium where no single node dominates the resources of networks. By leveraging these insights, modern approaches to congestion control go beyond manually tuned algorithms, using computational methods to synthesize optimal control strategies that balance throughput and delay in complex network environments. As illustrated in Figure 4b, the iterative adjustments shown by the arrows lead to optimal resource allocation as the system moves towards the fairness point through a dual optimization process. This ensures the system converges to a stable allocation guided by the convex optimization and eigenvector analysis. The bold arrow points to the direction where the system reaches optimal performance (maximum efficiency without congestion), representing the convergence towards fairness under dynamic network conditions. TCP ex Machina in [192] introduces Remy, an innovative approach to congestion control.