RT Journal Article
T1 Functions as a service for distributed deep neural network inference over the cloud-to-things continuum
A1 Bueno Calvente, Altair
A1 Rubio-Muñoz, Bartolomé
A1 Martín-Fernández, Cristian
A1 Díaz-Rodríguez, Manuel
K1 Redes de ordenadores
AB The use of serverless computing has been gaining popularity in recent years as an alternative to traditional Cloud computing. We explore the usability and potential development benefits of three popular open-source serverless platforms in the context of IoT: OpenFaaS, Fission, and OpenWhisk. To address this we discuss our experience developing a serverless and low-latency Distributed Deep Neural Network (DDNN) application. Our findings indicate that these serverless platforms require significant resources to operate and are not ideal for constrained devices. In addition, we archived a 55% improvement compared to Kafka-ML's performance under load, a framework without dynamic scaling support, demonstrating the potential of serverless computing for low-latency applications.
PB Wiley
YR 2024
FD 2024-02-11
LK https://hdl.handle.net/10630/30436
UL https://hdl.handle.net/10630/30436
LA spa
NO Bueno A, Rubio B, Martín C, Díaz M. Functions as a service for distributed deep neural network inference over the cloud-to-things continuum. Softw: Pract Exper. 2024; 1-15. doi: 10.1002/spe.3318
NO Funding for open Access charge: Universidad de Málaga / CBUA.Ministerio de Ciencia, Innovación y Universidades
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026