Ricerc@Sapienza

Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices during their activity. In this work we focus on a new type of data, available thanks to the definition of the novel SRv6 paradigm, referred to as SRv6 Traffic Counters (SRTCs). SRTCs provide aggregated measurements related to forwarding operations performed by SRv6 routers.

In this work we investigate the use of Segment Routing version 6 (SRv6) interface counters to improve the Traffic Matrix (TM) assessment of an ISP network. SRv6 is a source routing solution for IPv6 networks: it allows defining a network path as a sequence of routers, represented by Segments Identifiers (SID), to be crossed, i.e. a Segment List (SL). SRv6 provides a new set of interface counters able to measure the packets/ bytes of Ingress/Egress flows exploiting the forwarding operations performed on the Segment Identifiers (SIDs) reported in the SLs.