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Inferring the Migration of Services in Pervasive Edge Applications
  • Georgios Boulougaris ,
  • Kostas Kolomvatsos
Georgios Boulougaris
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Kostas Kolomvatsos
University of Thessaly

Corresponding Author:[email protected]

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The coexistence of the Internet of Things (IoT) and Edge Computing (EC) 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 needs and requests of end users or applications. The definition of an effective framework for services management in the distributed edge nodes is a necessary condition to achieve the aforementioned goals. This is because services should be placed at the appropriate locations in order to meet the challenges defined by the demand for them. The discussed framework ought to address the trade-off between overheads related to services migration/replication and data transmission in order to deliver the most efficient setup for edge nodes. In this paper, we propose a proactive statistical model that creates a ‘map’ for allocating the available services upon the observed demand and supports edge nodes to decide when and where it is necessary to migrate/replicate a sub-set of services. Our aim is to place services at locations where an increased demand imposed by the requested processing activities, however, under the uncertainty about the future evolution of the requests. We elaborate on the evaluation of the proposed model and offer a comparative assessment with relevant schemes adopting real datasets. Our experimental evaluation 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 end users.