Autof

Autof is a modular software framework intended to assist in building autonomous optimization systems. It provides tools to define objectives, monitor performance, and automatically adjust configurations or parameters in real time. Autof emphasizes portability across platforms and compatibility with a variety of optimization strategies, from heuristic search to machine learning-based planning.

The name Autof derives from "automatic optimization framework" and is used in both academic literature and industry discussions as a generic reference rather than a single product.

At its core, Autof offers a decoupled architecture with a decision engine, an evaluation module, a policy layer, and adapters for data sources and actuators. Plugins supply objective functions, search strategies, and execution targets.

Autof operates by collecting telemetry, evaluating configurations against objectives, and applying changes subject to safety constraints and rollback mechanisms. It supports Bayesian optimization, reinforcement learning, and rule-based policies, enabling continuous experimentation while maintaining reproducibility through logging and reproducible seeds.

Common use cases include cloud service autoscaling, database query tuning, and energy-management systems. Critics note challenges in ensuring stability, safety, and explainability in autonomous configurations. Proponents highlight reduced manual tuning effort and the ability to adapt to changing workloads.