Detecting application transitions and identifying application types for intent-based network assurance: A machine learning perspective
Bibliographic record
Abstract
• Developed Monitoring tool collectors for fine-grained edge workload monitoring • Lightweight pipeline for intent-based assurance on resource-constrained devices • Real-time detection of application transitions using an autoencoder model • Fast and accurate Application Type Identification via Random Forest classifier • Public AIMED-2025 dataset with 9 workloads on RPi to support the research community Intent-Based Networking (IBN) enables agile and policy-driven network management by translating high-level intents into concrete configurations and continuously validating their compliance. A critical limitation in current Intent-Based Network Assurance (IBNA) systems is the lack of real-time application-level awareness, particularly in dynamic edge environments where AI workloads frequently change. In this work, we address this limitation by introducing a lightweight, monitoring-driven pipeline that enables the detection of application transitions and identification of newly active application types on edge devices. In collaboration with Netdata engineers, we develop multimetric data collectors using Netdata, an open-source platform for real-time system and application monitoring. These collectors capture application-agnostic system metrics with minimal overhead, forming the foundation for real-time alerting and dynamic network adaptation. Our proposed pipeline transforms raw monitoring data into fixed-length vectorized multivariate time series. An undercomplete autoencoder is then used to detect changes in system behavior indicative of application transitions, followed by a Random Forest classifier that labels the newly active application based on its resource usage profile. To support reproducibility, we construct and publicly release the AIMED-2025 dataset, which includes monitoring data from seven MediaPipe-based edge AI applications and two idle states, all executed on a Raspberry Pi. Experimental evaluation demonstrates that our method achieves 100% accuracy in both Application Transition Detection and Application Type Identification using only a three-second observation window. Furthermore, the system exhibits sub-second training times and millisecond-scale inference latency, making it suitable for real-time deployment on resource-constrained edge devices. Once an application change is detected and identified, the IBNA system can automatically alert network administrators and trigger dynamic reconfiguration of network resources to meet the specific performance, security, and connectivity requirements of the active application. By integrating application-level awareness into IBNA, this work advances the state of the art in intent-driven network management and enables more adaptive, efficient, and reliable operation of edge AI systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".