Recovering Missing Monitoring Data to Enhance Service Provisioning in the Edge-to-Cloud Continuum

Efficient service provisioning in the Edge-to-Cloud Continuum is of utmost importance for modern applications. While sensible decisions can be taken if enough monitoring data is collected, maintaining continuous telemetry data streams amidst the continuum's complexity is challenging. This paper introduces CRISP (reConstructing Resource Information for Service Placement), a solution combining data reconstruction and service placement strategies to optimize decisions despite incomplete monitoring data. CRISP utilizes Convolutional Neural Networks and Long Short-Term Memory models for data reconstruction, integrating them with a heuristic algorithm that selects nodes for service component placement. Numerical results demonstrate CRISP's efficacy in optimizing service provisioning despite missing data, contributing to enhanced resource utilization and service performance in the considered context.