Consistent updates for software-defined networks: change you can believe in!
Abstract
Configuration changes are a common source of instability in networks, leading to broken connectivity, forwarding loops, and access control violations. Even when the initial and final states of the network are correct, the update process often steps through intermediate states with incorrect behaviors. These problems have been recognized in the context of specific protocols, leading to a number of point solutions. However, a piecemeal attack on this fundamental problem, while pragmatic in the short term, is unlikely to lead to significant long-term progress.
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
DeepLog shows that systems can learn usual log patterns and flag departures from them, although reliability still depends on timing robustness and parser accuracy.
“DeepLog shows that systems can learn usual log patterns and flag departures from them, although reliability still depends on timing robustness and parser accuracy [63].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [63]
AIOps pipelines rarely work with raw logs in their original form; they usually introduce at least three intermediate steps: parsing or templating, aggregation, and detection or scoring.
“For this reason, AIOps pipelines rarely work with raw logs in their original form.They usually introduce at least three intermediate steps: parsing or templating, which turns free text into structured events, aggregation, which forms sequences or distributions at service, host, or request level, and detection or scoring, which identifies anomalies that deserve escalation [62], [129], [63], [64].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [63]
Logs remain the most common operational signal, and also one of the least forgiving.
“Logs remain the most common operational signal, and also one of the least forgiving [62], [63], [127], [128].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [63]
Deep sequence models such as DeepLog showed that event-sequence modelling can detect anomalies without hand-written rules.
“Deep sequence models such as DeepLog showed that event-sequence modelling can detect anomalies without hand-written rules [63].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [63]
Cited by
Sorted by citations
A roadmap for traffic engineering in SDN-OpenFlow networks
Akyildiz, I. F., Lee, A., Wang, P., Luo, M., & Chou, W. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.06.002 · Cited by 536 · see citing works
Hybrid SDN evolution: A comprehensive survey of the state-of-the-art
Khorsandroo, S., Sánchez, A. G., Tosun, A. S., Arco, J., & Doriguzzi-Corin, R. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107981 · Cited by 111 · see citing works
Lossless migrations of link-state IGPs
Vanbever, L., Vissicchio, S., Pelsser, C., Francois, P., & Bonaventure, O. (2012) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2012.2190767 · Cited by 46 · see citing works
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
- Authors
- Reitblatt, M., Foster, N., Rexford, J., & Walker, D.
- Affiliations
- Cornell University (US)
- Princeton University (US)
- Venue
- Proceedings of the 10th ACM Workshop on Hot Topics in Networks
- Publisher
- ACM
- Published
- 2011-11-14
- Pages
- 1-6
- OpenAlex
- W2141988289
- Cited by
- 163
- References
- 0
- Citations by year
- 2025: 1 · 2024: 6 · 2023: 2 · 2022: 6 · 2021: 9 · 2020: 5 · 2019: 17 · 2018: 17 · 2017: 15 · 2016: 21 · 2015: 10 · 2014: 23 · 2013: 12 · 2012: 19
- FWCI
- 15.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Hot Topics in Networks (HotNets) (publishedVersion)
- Funders
- Office of Naval Research (N00014-09-1-0770)
- Division of Computer and Network Systems (CNS-1111698CNS-1111520)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Keywords
- software-defined networking, consistency, network instability, forwarding loops, network state transitions
About these checks
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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