A Pervasive Edge Computing Model for Proactive Intelligent Data Migration

Currently, there is a great attention of the research community for the intelligent management of data in a context-aware manner at the intersection of the Internet of Things (IoT) and Edge Computing (EC). In this article, we propose a strategy to be adopted by autonomous edge nodes related to their decision on what data should be migrated to specific locations of the infrastructure and support the desired requests for processing. Our intention is to arm nodes with the ability of learning the access patterns of offloaded data-driven tasks and predict which data should be migrated to the original ‘owners’ of tasks. Naturally, these tasks are linked to the processing of data that are absent at the original hosting nodes indicating the required data assets that need to be accessed directly. To identify these data intervals, we employ an ensemble scheme that combines a statistically oriented model and a machine learning scheme. Hence, we are able not only to detect the density of the requests but also to learn and infer the ‘strong’ data assets. The proposed approach is analyzed in detail by presenting the corresponding formulations being also evaluated and compared against baselines and models found in the respective literature.