Published October 12, 2020 | Version v1
Preprint Open

Energy-efficient Deployment of IoT Applications in Edge-based Infrastructures: A Software Product Line Approach

  • 1. Universidad de Málaga

Description

This is a pre-print, please access and cite the published version.

In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. However, current task offloading approaches are often quite simple. They neither consider the high diversity of hardware and software technologies present in edge network devices, nor take into account that some tasks may require some specific software and hardware infrastructure to be executed. This paper proposes a task offloading process that leans on Software Product Line technologies, which are a very good option to model the variability of software and hardware present in edge environments. Firstly, our approach automates the separation of application tasks, considering the data and operation needs and restrictions among them, and identifying the hardware and software resources required by each task. Secondly, our approach models and manages separately the infrastructure available for task offloading, as a set of nodes that provide certain hardware and software resources. This separation allows to reason about alternative offloading of tasks with different hardware and software resource requirements, in heterogeneous nodes and minimizing energy consumption. In addition, the offloading process considers alternative implementations of tasks to choose the one that best fits the hardware and software characteristics of available edge network infrastructure. The experimental results shows that our approach reduces the energy consumption in the user node by approximately 41%–62%, and the energy consumption of the devices involved in a task offloading solution by 34-48%.

Notes

This work is supported by the MEDEA RTI2018-099213-B-I00 (co-funded by FEDER funds), LEIA UMA18-FEDERJA-157 (co-funded by FEDER funds) and RHEA P18-FR-1081 (MCI/AEI/FEDER, UE)

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Additional details

Related works

Is previous version of
Journal article: 10.1109/JIOT.2020.3030197 (DOI)

Funding

DAEMON – Network intelligence for aDAptive and sElf-Learning MObile Networks 101017109
European Commission