[Paper] A Low-Latency Semantic State Estimator using Latent Predictive Learning for Dynamic Network Monitoring and Orchestration
Source: arXiv - 2606.08869v1
Overview
Closed-loop network monitoring and orchestration increasingly require semantic interpretations of live telemetry beyond raw counter collection. However, dynamic cloud-edge environments change both the active node set and the monitoring query at runtime, while control loops demand bounded millisecond-scale responses. We introduce a latent predictive state estimator (LPSE) for dynamic network monitoring and orchestration, built on latent predictive learning over streaming telemetry. The framework converts variable-cardinality node telemetry into topology-adaptive temporal representations, fuses them with monitoring questions, and returns bounded answers from a semantic codebook instead of autoregressive text generation. This design enables fixed-cost, single-pass inference while preserving semantic interpretability. By operating on permutation-invariant, slot-routed node representations keyed by stable identity, the model maintains a fixed input space and generalizes to node addition, removal, and reordering without retraining. Experimental results on a multi-node Kubernetes cluster show semantic prediction accuracy of 82.42% at approximately 41$\times$ lower mean inference latency and 15$\times$ smaller memory footprint compared with a deployable 4B LLM endpoint.
Key Contributions
This paper presents research in the following areas:
- cs.DC
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.DC.
Authors
- Hari Madhukumar
- Haiyuan Li
- Xiaolan Liu
- Andy Corston-Petrie
- Dimitra Simeonidou
Paper Information
- arXiv ID: 2606.08869v1
- Categories: cs.DC
- Published: June 7, 2026
- PDF: Download PDF