RT Journal Article
T1 Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models
A1 Cañete Valverde, Ángel Jesús
A1 Amor-Pinilla, María Mercedes
A1 Fuentes-Fernández, Lidia
K1 Computación en nube
K1 Internet de las cosas
AB Edge Computing proposes to use the nearby devices in the frontier/Edge of the access network for deploying application tasks of IoT-based systems. However, the functionality of such cyber–physical systems, which is usually distributed in several devices and computers, imposes specific requirements on the infrastructure to run properly. The evolution of an application to meet new user requirements and the high diversity of hardware and software technologies in the IoT/Edge/Cloud can complicate the deployment of continuously evolving applications. The aim of our approach is to apply Multi Layer Feature Models, which capture the variability of applications and the software and hardware infrastructure, to support the deployment in edge-based environments of cyber–physical applications. With this multi-layered approach is possible to support the evolution of application and infrastructure independently. Considering that IoT/Edge/Cloud infrastructures are usually shared by many applications, the deployment process has to assure that there will be enough resources for all of them, informing developers about the feasible alternatives. We provide four modules so that the developer can calculate what is the configuration of minimal set of devices supporting application requirements of the evolved application. In addition, the developer can find what is the application configuration that can be hosted in the current infrastructure. The successive solutions of continuous deployment generated by our approach pursue the reduction of the system energy footprint and/or execution latency.
PB Elsevier
YR 2022
FD 2022-01
LK https://hdl.handle.net/10630/22972
UL https://hdl.handle.net/10630/22972
LA eng
NO Angel Cañete, Mercedes Amor, Lidia Fuentes, Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models, Journal of Systems and Software, Volume 183, 2022
NO This work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds, Spain LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081. Funding for open access charge: Universidad de Málaga/CBUA.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026