Towards Intelligent Pulverized Systems: a Modern Approach for Edge-Cloud Services

Emerging trends are leveraging the potential of the edge-cloud continuum to foster the creation of smart services capable of adapting to the dynamic nature of modern computing landscapes. This adaptation is achievable through two primary methods: by leveraging the underlying architecture to refine machine learning algorithms, and by implementing machine learning algorithms to optimize the distribution of resources and services intelligently. This paper explores the latter approach, focusing on recent advancements in pulverized architecture, collective intelligence, and many-agent reinforcement learning systems. This novel trend, which we refer to as intelligent pulverized system (IPS), aims to create a new generation of services that can adapt to the complex and dynamic nature of the edge-cloud continuum. Our proposed learning framework integrates many-agent reinforcement learning, graph neural networks, and aggregate computing to create intelligent services tailored for this environment. We discuss the application of this framework across different levels of the pulverization model, illustrating its potential to enhance the adaptability and efficiency of services within the edge-cloud continuum.