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

TL;DR

This paper presents a methodology for optimizing complex, hybrid applications across the Edge-to-Cloud continuum, focusing on performance, resource, and cost trade-offs, demonstrated through a plant identification application.

Contribution

It introduces a systematic approach and an extension of E2Clab for optimizing real-world applications on the Edge-to-Cloud infrastructure.

Findings

Effective configuration analysis in a controlled testbed.

Demonstrated optimization of a plant identification application.

Methodology generalizes to other Edge-to-Cloud applications.

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.