Presentation
Framework for Integrating Machine Learning Methods for Path-Aware Source Routing
SessionThe 11th Annual International Workshop on Innovating the Network for Data Intensive Science - INDIS
DescriptionSince 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. 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. In this paper, we leverage Hecate to practically demonstrate TE on a real network, collaborating with PolKA, a source routing protocol tool. With real-time traffic statistics, Hecate uses this data to compute optimal paths that are then communicated to PolKA to allocate flows. This work proves valuable for truly engineered self-driving networks helping translate theory to practice.
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