SUSE/phoebe

Implements autonomous system tuning through a plugin-based architecture that continuously collects network and system telemetry, applies basic machine learning inference (weighted inputs with bias) to predict optimal kernel parameters, and automatically reconfigures the system with configurable grace periods to prevent oscillation. Supports both training and inference modes with tunable collection intervals, accuracy thresholds, and approximation functions to match observed metrics against learned parameter sets. Designed for self-healing capabilities on resource-constrained or misconfigured systems by treating tuning as an ongoing feedback loop rather than manual intervention.

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