Towards energy-efficient serverless clouds: A reinforcement learning-based scheduling approach

The serverless computing paradigm has transformed cloud infrastructure by favoring dynamic resource allocation and automatic scaling, reducing the complexity of infrastructure management. However, as workloads with high computing demands and rapid instantiation requirements become more prevalent, energy efficiency remains a critical challenge. Inefficient workload scheduling can lead to suboptimal resource utilization and increased power consumption. In this respect, traditional scheduling policies often struggle to adapt to the dynamic and unpredictable nature of serverless workloads, highlighting the need for more energy-aware scheduling strategies. In this work, we propose a Reinforcement Learning (RL)-assisted scheduling approach to enhance energy efficiency in serverless cloud computing. We develop a Deep Q-Network (DQN)-based scheduler that continuously learns workload placement strategies, minimizing cluster-wide power consumption, and consolidating workload across available Nodes. We implement our approach as a custom scheduling plugin for Kubernetes, ensuring seamless integration with Knative-based serverless workloads. We evaluate our approach using live system telemetry and compare its performance against baseline scheduling techniques. The results show that our RL-based scheduler outperforms the default Kubernetes scheduler by 9.5 % in the total cluster CPU consumption, allowing potential energy savings.