Enabling AI to Adapt in Cloud and Edge Computing Systems

What is it about?

Modern applications—from smart cities to healthcare—often rely on a mix of cloud and edge computing to meet demands for speed and efficiency. But managing where and how jobs (like processing data) are executed in such hybrid systems is incredibly complex. This paper presents a smarter way to handle that: by using deep reinforcement learning (DRL), a form of AI that learns through trial and error. The challenge is that DRL agents often need to be re-trained from scratch whenever the computing environment changes, which is slow and costly. To solve this, we developed a method that teaches these agents to understand the environment in a more general, flexible way—so they can adapt and transfer their knowledge across different systems without starting over. Our approach helps the system place jobs more efficiently, maintain service quality, and meet deadlines even when infrastructure or workloads change. In tests, our method outperformed both traditional rule-based approaches and standard AI models, making it a strong candidate for real-world use in future cloud-edge technologies.

Featured Image

Photo by Growtika on Unsplash

Photo by Growtika on Unsplash

Why is it important?

As cloud and edge systems grow increasingly complex and dynamic, managing computational resources efficiently becomes a major challenge. Current AI solutions often fail to adapt when infrastructures change, requiring costly and time-consuming retraining. What makes our work unique is that it introduces a way to build adaptable AI agents that can generalize across different computing environments. By learning infrastructure-agnostic representations of the system, our approach allows reinforcement learning agents to retain their knowledge and perform effectively even when the underlying setup changes—something existing solutions struggle to handle. This significantly reduces operational overhead and makes AI-based job placement viable in real-world, ever-changing systems. The ability to transfer knowledge between environments marks a step forward in creating robust and reusable AI for cloud-edge computing.