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California fault lines: understanding the causes and impact of network failures
Open accessAbstract
Of the major factors affecting end-to-end service availability, network component failure is perhaps the least well understood. How often do failures occur, how long do they last, what are their causes, and how do they impact customers? Traditionally, answering questions such as these has required dedicated (and often expensive) instrumentation broadly deployed across a network.
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“These systems learn or construct event-based causal graphs and use these graphs to generate interpretable root cause analysis (RCA) candidate models.They do this rather than simply relying on seemingly convincing correlations [124], [125], [126], [59].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [126]
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“Trace-based RCA benchmarks expose causal structure more directly.”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [126]
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In the citing paper
“TraceRCA localises microservice root causes using traces and related signals [126].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [126]
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Cited by
Sorted by citations
Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety
Bilal, M., Crowcroft, J., Wang, R., Xu, X., & Dustdar, S. (2027) · Computer Science Review · doi:10.1016/j.cosrev.2026.101075 · Cited by 10
Metadata
- Authors
- Turner, D., Levchenko, K., Snoeren, A. C., & Savage, S.
- Affiliations
- University of California San Diego (US)
- Venue
- Proceedings of the ACM SIGCOMM 2010 conference
- Publisher
- ACM
- Published
- 2010-08-30
- Pages
- 315-326
- OpenAlex
- W2106263082
- Cited by
- 164
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 13 · 2020: 11 · 2019: 8 · 2018: 16 · 2017: 17 · 2016: 16 · 2015: 12 · 2014: 16 · 2013: 18 · 2012: 7
- FWCI
- 10.39
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGCOMM Conference (SIGCOMM) (publishedVersion)
- Funders
- University of California, San Diego
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Agent-Based Network Management
- Keywords
- fault detection, failure frequency, failure duration, failure causes, customer impact, network instrumentation, telecommunications networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: California fault lines: understanding the causes and impact of network failures. https://app.cochat.ai/papers/10.1145/1851182.1851220
- The paper
- Turner, D., Levchenko, K., Snoeren, A. C., & Savage, S. (2010). California fault lines: understanding the causes and impact of network failures. Proceedings of the ACM SIGCOMM 2010 conference. https://doi.org/10.1145/1851182.1851220
