Decentralized Multi-Agent Reinforcement Learning for the Green Serverless Cloud-Edge Continuum

The Cloud-Edge Continuum systems are inherently complex and massive, often featuring federated multi-provider stakeholders (e.g. cloud/edge service providers, energy providers), heterogeneous platforms, and dynamic infrastructures; this significantly increases the complexity of developing, deploying, and managing applications. The Serverless computing offers a powerful tool to simplify and speed up the Continuum application development. However, existing scheduling mechanisms for Serverless platforms focus primarily on performance metrics such as latency, model accuracy, and throughput, often neglecting critical factors such as energy efficiency and sustainability. This gap is further exacerbated in Continuum environments, where computational nodes may rely on unpredictable and intermittent green energy sources, leading to availability bottlenecks and energy constraints. This work investigates the design of a decentralized green energy-aware approach for scheduling Serverless functions across the Cloud-Edge Continuum. To achieve this, we introduce a formal model of the green energy-aware workload scheduling problem. We then develop a consensus-based upper confidence bound (UCB) approach for cooperative multi-agent reinforcement learning (MARL) that leverages distributed agents to consider energy awareness and quality-of-service (QoS) requirements of different functions into their scheduling decisions. To demonstrate the practicality of our approach, we implement a real-world prototype using a cluster of Raspberry Pis, Cloud servers, Kubernetes, and OpenFaaS. Experimental results show that our approach maximizes the green energy utilization by (44%) and reduces total latency by (25%) compared to the centralized technique, highlighting its energy efficiency, scalability, and overall sustainability in Continuum settings.