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From Path-Aware to Application-Aware Source Routing using Traffic Classes
SessionThe 12th Annual International Workshop on Innovating the Network for Data-Intensive Science (INDIS)
DescriptionSince the advent of software defined networking (SDN), its architecture allows better network flexibility, capacity planning and improved performance, especially for traffic engineering. Additionally, network operators are using in-band network telemetry (INT) to build efficient programmable networks via controlling various network flow patterns. The capability of
programmable data plane intertwined with Artificial Intelligence (AI) have established self-driven network services, such as Hecate tool, for seamless and salable network management and control. In this paper, we explore different in-production traffic patterns to perform AI-driven traffic control and engineering. We then develop a novel queuing algorithm based on the observed traffic patterns to enhance traffic engineering of Hecate with source routing at the edge. This work feeds into P4 programmability to show how source routing can use machine learning to deploy self-engineering networks.
programmable data plane intertwined with Artificial Intelligence (AI) have established self-driven network services, such as Hecate tool, for seamless and salable network management and control. In this paper, we explore different in-production traffic patterns to perform AI-driven traffic control and engineering. We then develop a novel queuing algorithm based on the observed traffic patterns to enhance traffic engineering of Hecate with source routing at the edge. This work feeds into P4 programmability to show how source routing can use machine learning to deploy self-engineering networks.
