An Inference Mechanism for Proactive Service Migration at the Edge

Abstract

The coexistence of the Internet of Things and Edge Computing aims to offer a processing infrastructure close to end users that will improve the performance of applications and limit the latency in the provision of services. Services are adopted to assist in the execution of tasks imposed by the requests of end users/applications. The implementation of an effective framework for services management in the distributed edge nodes is necessary to achieve the aforementioned goals. The discussed framework ought to address the trade-off between overheads related to services migration/replication and data transmission. In this paper, we propose a proactive statistical model for allocating the available services upon the observed demand and supporting edge nodes to decide when and where it is necessary to migrate/replicate them. Our aim is to place services at locations where an increased demand is observed, however, under the uncertainty about the future evolution of the incoming requests. We elaborate on the evaluation of the proposed model and provide a comparative assessment with relevant schemes adopting real datasets. Our experimental validation demonstrates that our approach reinforces the heterogeneous engaged edge nodes to correctly infer the time instance and the location when/where services should be migrated/replicated to meet the dynamics of their demand. The interesting is that the proposed model achieves encouraging outcomes when it is adopted to cope with the mobility of users.