Intelligent Scheduling of Serverless Computing in Computing Power Networks

In the rapidly evolving landscape of computing power networks, applications are becoming more complex and diverse. Consequently, this evolution has led to a growing need for sophisticated intelligent scheduling mechanisms to adeptly manage these networks. Utilizing intelligent algorithms has become pivotal in ensuring effective and reliable computational services for these intricate networks. This paper addresses the pressing issue of intelligent scheduling in serverless computing power networks. We explore a computing power network system model based on serveless computing, formulate the intelligent task scheduling problem as a multi-objective optimization problem, present a solution that designs task scheduling policies using the Deep Deterministic Policy Gradient (DDPG) algorithm from reinforcement learning. Additionally, we perform experiments to validate the practicality and effectiveness of our proposed approach.