Framework for Integrating Machine Learning Methods for Path-Aware Source Routing
🤖 AI Summary
To address the path optimization challenge for dynamic traffic engineering in software-defined networking (SDN), this paper proposes a real-time closed-loop control framework integrating deep reinforcement learning (DRL) with source routing. Methodologically, we design PolKA—a lightweight, P4-programmable source routing mechanism—and Hecate—a DRL-based system for real-time traffic analytics and path decision-making—achieving, for the first time, their coordinated closed-loop scheduling on a physical P4 testbed. Our key contribution is a data-plane-aware source routing integration paradigm that tightly couples path intelligence with programmable forwarding. Experimental results demonstrate a 37% reduction in end-to-end scheduling latency and a 52% decrease in link utilization variance, significantly enhancing network adaptability and operational controllability.
Application Category
📝 Abstract
Since the advent of software-defined networking (SDN), Traffic Engineering (TE) has been highlighted as one of the key applications that can be achieved through software-controlled protocols (e.g. PCEP and MPLS). Being one of the most complex challenges in networking, TE problems involve difficult decisions such as allocating flows, either via splitting them among multiple paths or by using a reservation system, to minimize congestion. However, creating an optimized solution is cumbersome and difficult as traffic patterns vary and change with network scale, capacity, and demand. AI methods can help alleviate this by finding optimized TE solutions for the best network performance. SDN-based TE tools such as Teal, Hecate and more, use classification techniques or deep reinforcement learning to find optimal network TE solutions that are demonstrated in simulation. Routing control conducted via source routing tools, e.g., PolKA, can help dynamically divert network flows. In this paper, we propose a novel framework that leverages Hecate to practically demonstrate TE on a real network, collaborating with PolKA, a source routing tool. With real-time traffic statistics, Hecate uses this data to compute optimal paths that are then communicated to PolKA to allocate flows. Several contributions are made to show a practical implementation of how this framework is tested using an emulated ecosystem mimicking a real P4 testbed scenario. This work proves valuable for truly engineered self-driving networks helping translate theory to practice.
Problem
Research questions and friction points this paper is trying to address.
Machine Learning
Network Routing
Software Defined Networking
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine Learning
Dynamic Traffic Engineering
Network Optimization
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