CCA Reimagined: An Exploratory Study of Large Language Models for Congestion Control

2026-04-04

AI Summary

This work addresses the challenge of simultaneously achieving low latency and high throughput in highly dynamic networks, where traditional congestion control algorithms often fall short. It presents the first systematic exploration of leveraging large language models (LLMs) for congestion control, proposing an LLM-driven strategy that integrates a dynamic triggering mechanism with carefully constrained control freedom. The approach initially focuses on the congestion avoidance phase and is subsequently extended to multi-phase coordinated control, enabled through tailored instruction engineering and deployed via network simulation. Experimental results demonstrate that, across both static and dynamic network traces, the proposed method reduces latency by up to 50% compared to conventional algorithms while incurring less than 0.3% throughput loss, thereby significantly enhancing overall performance in dynamic environments.

Abstract

In this paper, we conduct an emulation-guided study to systematically investigate the feasibility of Large language model (LLM)-driven congestion control. The exploration is structured into two phases. The first phase derisks the whole capability where we isolate the role of LLM on a single yet crucial congestion avoidance phase so that we can safely examine when to invoke the LLM, what information to provide, and how to formulate LLM instructions. Based on the gained insights, we extend LLM's role to multiple congestion control phase and propose a more generic LLM-based congestion control policy. Our evaluation on both static and dynamic network traces demonstrates that the LLM-based solution can reduce latency by up to 50\% with only marginal throughput sacrifice (e.g., less than 0.3\%) compared to traditional CCAs. Overall, our exploration study confirms the potential of LLMs for adaptive and general congestion control, demonstrating that when granted appropriate control freedom and paired with an effective triggering mechanism, LLM-based policies achieve significant performance gains, particularly under highly dynamic network conditions.

Problem

• congestion control
• large language models
• network latency
• throughput
• dynamic networks

Innovation

• Large Language Models
• Congestion Control
• Network Emulation
• Adaptive Networking
• LLM-based Policy