Detecting application transitions and identifying application types for intent-based network assurance: A machine learning perspective

• 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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