SRmesh: Deterministic and Efficient Diagnosis of Latency Bottleneck Links in SRv6 Networks
Segment Routing over IPv6 (SRv6) has attracted more attention from network operators. Diagnosing performance bottlenecks for SRv6 tunnels is critical to maintaining network quality. However, SRv6 introduces priority policies, special forms of multipath routing, and SR-based network slicing, all of which make existing methods difficult to apply. In this paper, we present SRmesh, a framework for diagnosing latency bottleneck links specifically tailored for SRv6 tunnel performance analysis. First, we adopt SR-based probing to deterministically control routing paths and ensure that probe packets emulate real production traffic. Second, we employ a latency-based multipath inference to resolve routing ambiguities caused by SR. Third, we introduce a topology-independent progressive diagnosis that incrementally reuses probe results to reduce redundant measurements, optimizing diagnostic overhead for large-scale SRv6 overlay networks. We implement a prototype of SRmesh and conduct extensive evaluations on real network topologies. The results indicate that SRmesh achieves high diagnostic accuracy with up to a 91.9% reduction in probe overhead, demonstrating its practicality and scalability in large-scale SRv6 environments.
Authors
- Xingang Shi (ORCID: https://orcid.org/0000-0001-6487-9526)
- Zhang Han (ORCID: https://orcid.org/0000-0002-7619-3756)
- Zhiliang Wang (ORCID: https://orcid.org/0000-0002-1233-621X)
- Yahui Li (ORCID: https://orcid.org/0000-0002-7094-4053)
- Xia Yin (ORCID: https://orcid.org/0000-0001-9784-8742)
- Jian Wu (ORCID: https://orcid.org/0000-0002-3394-1507)
- Yao Tong (ORCID: https://orcid.org/0000-0001-8499-015X)
- Kaiyang Zhao (ORCID: https://orcid.org/0009-0008-9797-0965)
Institutions
- Tsinghua University (CN)
Publication Details
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1109/icnp65844.2025.11192455
- Citations
- 1
- Primary Topic
- Software-Defined Networks and 5G
- Type
- article
- Field-Weighted Citation Impact
- 1.49