A demand aware services placement model in Pervasive Edge Computing

Abstract

Nowadays, one can observe the convergence of the Internet of Things (IoT) and Edge Computing (EC) infrastructures towards establishing a data collection and processing ecosystem in close proximity to end users. The aim is to enhance the performance of the supported applications by reducing the latency in data processing and service delivery. Various services can be employed to facilitate the execution of tasks prompted by end users or any type of external applications. Those services are mainly present at EC nodes that become the hosts of the data collected by IoT devices, the executors of the desired tasks and the intermediaries when transferring the discussed data to the Cloud back end. It is obvious that the implementation of an efficient framework for managing services across distributed edge nodes becomes imperative especially if we bear in mind that nodes are constrained devices and cannot host numerous services. In this paper, we introduce a proactive model designed to allocate the available services to core parts of the EC ecosystem based on the observed demand. This will give us the opportunity to determine ‘where’ to place any individual service putting it in locations (i.e., in EC nodes) where an increased demand is identified, while saving resources by restricting the number of nodes that become the final hosts (to avoid the flooding of the network). The paper delves into the evaluation of the proposed model, offering a comparative analysis with a baseline scheme utilizing real datasets. Through the envisioned experimental validation, the paper demonstrates that the proposed approach enhances the ability of diverse engaged edge nodes to accurately deduce the appropriate location for service placement.