Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum

TL;DR

This paper presents E2Clab, a platform that enables reproducible analysis and optimization of complex workflows across the Edge-to-Cloud continuum, addressing challenges in understanding end-to-end performance in interconnected digital infrastructures.

Contribution

It introduces a rigorous methodology and a comprehensive platform supporting deployment, analysis, and optimization across the Computing Continuum, validated with real-world use cases.

Findings

E2Clab allows understanding of performance variations by correlating parameters and infrastructure.

Preliminary results demonstrate improved performance analysis and optimization capabilities.

The platform supports the complete experimental cycle from deployment to analysis.

Abstract

Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the Digital Continuum, or the Transcontinuum). Understanding end-to-end performance in such a complex continuum is challenging. This breaks down to reconciling many, typically contradicting application requirements and constraints with low-level infrastructure design choices. One important challenge is to accurately reproduce relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. We introduce a rigorous methodology for such a process and validate it through E2Clab. It is the first platform to support the complete experimental cycle across the Computing Continuum: deployment, analysis, optimization. Preliminary results with real-life use cases show that E2Clab allows one to understand and improve performance, by correlating it to the parameter settings, the resource usage and the specifics of the underlying infrastructure.