Policy Compression for Low-Power Intelligent Scaling in Software-Based Network Architectures

Abstract

Modern networks, characterized by their complexity and heterogeneity, are transitioning from manual to automated and intelligent management, according to the vision of Autonomous Networks (ANs). Leveraging data-driven techniques, ANs aim to provide the "Zero-X" and "Self-X" experience, where intelligent and adaptable network operations are key cornerstones. Such is the case of intelligent resource scaling, where the goal is to optimize resource orchestration to maximize efficiency, reduce latency, and maintain high-quality service, even amid fluctuating network loads and changing service requirements. Unfortunately, current approaches for auto-scaling are computationally expensive to deploy in resource-constrained devices such as those found at the edge or beyond. This paper introduces an innovative four-phase approach to train a compact, data-driven Deep Reinforcement Learning (DRL) scaler that can be deployed on low-power devices. Our results demonstrate the scalability and efficiency of this model, achieving state-of-the-art scaling with up to 1003x fewer parameters, enhancing interpretability and computational efficiency, making it a robust solution for intelligent resource scaling in network environments. The resulting 1487x runtime speed improvement and 28.5x reduction in memory requirements allow the scaler to be deployed on low-power devices and still operate in real-time, which is essential for mission-critical and latency-sensitive applications.