Poster: ADN: Agent-Defined Networking

Abstract

Software-Defined Networking (SDN) has revolutionized network management by decoupling control logic from data forwarding. However, limited by the traditional controller paradigm, existing SDN controllers remain inherently static, relying on predefined rules. This rigidity makes them ill-equipped to handle unforeseen traffic patterns or emerging threats, often defaulting to generic actions that fail to address nuanced scenarios. Large-Language Models (LLMs), a group of models with billions of parameters that are trained on diverse datasets, are known to excel at performing complex tasks that require human-level reasoning and prior knowledge. With such powerful models assumed to encapsulate the collective knowledge of network operations within their parameters, one question that this work asks is "Are static networking rules provided by humans or by heuristics still relevant?" To answer this question, we propose a new logically centralized controller powered entirely by an LLM, called Agentic-Defined Networking (ADN). ADN introduces a novel architecture that integrates LLMs as the reasoning core of an SDN control plane implemented in a real network controller. To support the main thesis, we present preliminary results on ADN's performance on dealing with unseen malicious traffic and congestion-aware routing.