Abstract
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 a detailed description of different SRTCs types (SR.INT, PISD, PSID.TM and POL) is provided and their relationships is formalized. The theoretical framework deployed is used to identify, on the basis of network configuration parameters of both SRv6 and IGP protocols, the minimum set of independent SRTCs to characterize the Network Status : we show that about the 80% of counters can be neglected with no information loss. We also apply our framework to two use cases: i) Traffic Matrix (TM) Assessment and ii) Traffic Anomaly Detection. For the TM assessment, we show that in a partially deployed SRv6 scenario a specific type of SRTCs , i.e., PSID, is more reliable than other ones; on the contrary, in a fully deployed scenario POL and PSID.TM counters provide the full TM knowledge. For the Traffic Anomaly Detection case, we show that known solutions based on link load measurements can be improved when integrating SRTCs information.
Highlights
I N THE era Artificial Intelligence, Networking Monitoring is facing a significant change with respect to standard solutions used by network operators
The framework provides guidelines to detect relationships among different SRTCs on the basis of network paths and Segment Lists structure. In this way we provide a simple mechanism for the detection of the minimum set of useful SRTCs able to fully characterize the Network Status; this allows a significant amount of counters to be neglected, speeding up the data collecting phase performed by the Monitoring tool
All the results presented in the following are obtained under the following hypothesis: i) all the nodes in the domain are Segment Routing (SR) capable,7 ii) the central monitoring system simultaneously asks to all the nodes for the SRTCs, i.e., the values of the SRTCs are referred to the same time instant, iii) in the network none packet is dropped, and iv) sampling techniques are not applied in the update process of the SRTCs
Summary
I N THE era Artificial Intelligence, Networking Monitoring is facing a significant change with respect to standard solutions used by network operators. In this work we focus on novel aggregated measurements available in the Segment Routing version 6 (SRv6) network paradigm. The framework provides guidelines to detect relationships among different SRTCs on the basis of network paths and Segment Lists structure. In this way we provide a simple mechanism for the detection of the minimum set of useful SRTCs able to fully characterize the Network Status; this allows a significant amount of counters to be neglected, speeding up the data collecting phase performed by the Monitoring tool. POLVERINI et al.: THEORETICAL FRAMEWORK FOR NETWORK MONITORING EXPLOITING SEGMENT ROUTING COUNTERS cases that can be greatly impacted by SRTCs: i) Traffic Matrix Assessment (TMA) and ii) Traffic Anomaly Detection.
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