An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge

TL;DR

This paper introduces an ensemble scheme for proactive data migration at the edge, enabling autonomous nodes to predict and manage data access patterns for efficient processing in IoT and edge computing environments.

Contribution

It presents a novel ensemble approach combining statistical and machine learning models to identify dominant data assets and request densities for improved data migration strategies.

Findings

Effective identification of dominant data assets

Improved prediction of data access patterns

Enhanced data migration efficiency

Abstract

Nowadays, a significant focus within the research community on the intelligent management of data at the confluence of the Internet of Things (IoT) and Edge Computing (EC) is observed. In this manuscript, we propose a scheme to be implemented by autonomous edge nodes concerning their identifications of the appropriate data to be migrated to particular locations within the infrastructure, thereby facilitating the effective processing of requests. Our objective is to equip nodes with the capability to comprehend the access patterns relating to offloaded data-driven tasks and to predict which data ought to be returned to the original nodes associated with those tasks. It is evident that these tasks depend on the processing of data that is absent from the original hosting nodes, thereby underscoring the essential data assets that necessitate access. To infer these data intervals, we utilize an ensemble approach that integrates a statistically oriented model and a machine learning framework. As a result, we are able to identify the dominant data assets in addition to detecting the density of the requests. A detailed analysis of the suggested method is provided by presenting the related formulations, which is also assessed and compared with models found in the relevant literature.