Optimizing Energy Consumption of Edge-Cloud Environments: A comparative Study Between PPO and PSO

As the usage of the edge-cloud continuum increases, Kubernetes presents itself as a solution that allows easy control and deployment of applications in these highly-distributed and heterogeneous environments. In this context, Artificial Intelligence methods have been proposed to aid in the task allocation process to optimize different aspects of the system, such as application execution time, load balancing or energy consumption. In this paper, we present a comparative study focused on optimizing energy consumption through dynamic task allocation in a realistic V2X application scenario. We evaluate and compare two methods representing the most common algorithmic families for resource allocation: Particle Swarm Optimization (PSO) and Proximal Policy Optimization (PPO). Our methodology includes the design of a custom Kubernetes Operator to enforce the models’ node recommendations, allowing for rigorous, real-world validation against the base Kubernetes scheduler. Experiments demonstrate that while both PSO and PPO models successfully reduce energy consumption, PSO delivers the highest savings, reducing energy use by up to 20%. Crucially, our study highlights a key trade-off: although PSO is performance-superior for energy, the PPO model remains a faster and more computationally lightweight option that can be used widely on any kind of device, even with limited resources.