Can Reinforcement Learning be Generalized for Efficient Auto-Scaling in Containerized Clouds?
José Santos, Efstratios Reppas, Tim Wauters, Bruno Volckaert, Filip De Turck · 2025
The rapid adoption of containerized cloud environments requires robust and efficient Auto-Scaling (AS) mechanisms to ensure adequate resource utilization, high performance, and cost-effectiveness. Traditional AS approaches, often based on predefined thresholds, fail to adapt well to dynamic workloads. This paper investigates the potential of Reinforcement Learning (RL) as a generalized solution for efficient AS in containerized clouds. Building on previous studies, this paper examines whether RL approaches can learn adaptive scaling policies when trained on diverse workload datasets and tested across different scenarios. A Multi-Objective (MO) reward function has been designed to optimize key performance factors such as the application's response time, and resource utilization. The results demonstrate that RL algorithms can effectively balance competing objectives and adapt to changing workloads. The Latency strategy resulted in lower latency but required more pods (7.4) and slightly higher CPU usage (28.92%). In contrast, the Cost strategy minimized deployment costs with fewer pods (3.56) and lower CPU usage (24.45%). This study highlights the versatility and efficiency of RL in managing complex, real-time scaling decisions in containerized cloud infrastructures.