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OFCnetLLM: Large Language Model for Network Monitoring and Alertness
Open accessAbstract
The rapid evolution of network infrastructure is bringing new challenges and opportunities for e!cient network management, optimization, and security. With very large monitoring databases becoming expensive to explore, the use of AI and Generative AI can help reduce costs of managing these datasets. This paper explores the use of Large Language Models (LLMs) to revolutionize network monitoring management by addressing the limitations of query finding and pattern analysis. We leverage LLMs to enhance anomaly detection, automate root-cause analysis, and automate incident analysis to build a well-monitored network management team using AI. Through a real-world example of developing our own OFCNetLLM, based on the open-source LLM model, we demonstrate practical applications of OFCnetLLM in the OFC conference network. Our model is developed as a multiagent approach and is still evolving, and we present early results here.
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“Defences include cross-checking telemetry, validating formats, tracking provenance, and refusing action when evidence is too weak [83], [30], [31].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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“The shift from advisory operation to operational autonomy is therefore marked by the regime T execute k ̸ = ∅, where verification, rollout, rollback, and audit become binding constraints rather than optional safeguards [99], [31], [74].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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“This makes refusal and escalation measurable.A system can report when it stopped because evidence was missing, the budget was exhausted, the gate failed, or the estimated risk exceeded ϵ [30], [31], [80], [81], [82], [83].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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In the citing paper
“Autonomy changes which failures dominate [85], [43], [31].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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In the citing paper
“The qualitative takeaway is that gating and verification are not optional extras at higher rungs.They are binding constraints once T execute k is non-empty [31], [74].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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“Figure 6 sketches this review flow, which is the practical interface for accountability [74], [31].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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“This is useful only when the system constrains the output format and preserves provenance [92], [31].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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“Agentic evaluation requires labelled incidents, realistic tool interfaces, and success criteria aligned with operational practice [43], [79], [31].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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In the citing paper
“For AIOps, some gaps remain: tool-call traces, intermediate hypotheses, action-outcome simulation, and scoring that penalises unsafe actions even if the final diagnosis is right [135], [31], [74].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“These needs go back to Section III: safe autonomy can only be measured when tools, gates, and action envelopes are part of the benchmark [31], [43], [79].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [31]
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Citations
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References 8 · Cited by 5
Research works printed but not linked: 2
- Cisco. Trending analysis. https://www.cisco.com/c/en/us/solutions/collateral/serviceprovider/ visual-networking-index-vni/vnihyperconnectivity- wp.html, 2019.
- OpenAI, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. · arXiv
Metadata
- Authors
- Jun Yoon, H., Kiran, M., Ebling, D., & Breen, J.
- Affiliations
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Oak Ridge National Laboratory (US)
- Utah Education Network, Salt lake City, UT, USA
- University of Utah (US)
- Venue
- ACM SIGMETRICS Performance Evaluation Review
- Published
- 2025-08-26
- Volume
- 53
- Issue
- 2
- Pages
- 63-68
- ISSN
- 0163-5999 (ISSN-L)
- OpenAlex
- W4413768050
- Cited by
- 5
- References
- 8
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGMETRICS Performance Evaluation Review (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Energy (AC05-00OR22725, DE-AC05?, 00OR22725)
- Battelle (DE-AC05- 00OR22725, AC05-00OR22725, DE-AC05)
- UT-Battelle (DE-AC05, DEAC05-00OR22725, AC05-00OR22725)
- Advanced Scientific Computing Research (DE-AC05-00OR22725, DE-SC0024387, ERKJ435)
- Oak Ridge National Laboratory (AC05-00OR22725, DE-AC05-00OR22725)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- large language models, network monitoring, network management, anomaly detection, root cause analysis, incident analysis, multi-agent systems, generative AI, alerting
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- CoChat. (2026). Citation check: OFCnetLLM: Large Language Model for Network Monitoring and Alertness. https://app.cochat.ai/papers/10.1145/3764944.3764957
- The paper
- Jun Yoon, H., Kiran, M., Ebling, D., & Breen, J. (2025). OFCnetLLM: Large Language Model for Network Monitoring and Alertness. ACM SIGMETRICS Performance Evaluation Review. https://doi.org/10.1145/3764944.3764957
