Machine Learning Workflows in the Computing Continuum for Environmental Monitoring

Abstract

Cloud-Edge Continuum is an innovative approach that exploits the strengths of the two paradigms: Cloud and Edge computing. This new approach gives us a holistic vision of this environment, enabling new kinds of applications that can exploit both the Edge computing advantages (e.g., real-time response, data security, and so on) and the powerful Cloud computing infrastructure for high computational requirements. This paper proposes a Cloud-Edge computing Workflow solution for Machine Learning (ML) inference in a hydrogeological use case. Our solution is designed in a Cloud-Edge Continuum environment thanks to Pegasus Workflow Management System Tools that we use for the implementation phase. The proposed work splits the inference tasks, transparently distributing the computation performed by each layer between Cloud and Edge infrastructure. We use two models to implement a proof-of-concept of the proposed solution.