Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Abstract

In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-to-Cloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance trade-offs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
Fichier principal
Vignette du fichier
Cluster_2021_E2Clab_PlantNet_final.pdf (5.61 Mo) Télécharger le fichier
main.pdf (5.16 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03310540 , version 1 (03-08-2021)

Identifiers

Cite

Daniel Rosendo, Alexandru Costan, Gabriel Antoniu, Matthieu Simonin, Jean-Christophe Lombardo, et al.. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. Cluster 2021 - IEEE International Conference on Cluster Computing, Sep 2021, Portland, OR, United States. pp.23-34, ⟨10.1109/Cluster48925.2021.00043⟩. ⟨hal-03310540⟩
311 View
165 Download

Altmetric

Share

Gmail Facebook X LinkedIn More