  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.
