  Distributed infrastructures for computation and analytics are now evolving
towards an interconnected ecosystem allowing complex scientific workflows to be
executed across hybrid systems spanning from IoT Edge devices to Clouds, and
sometimes to supercomputers (the Computing Continuum). Understanding the
performance trade-offs of large-scale workflows deployed on such complex
Edge-to-Cloud Continuum is challenging. To achieve this, one needs to
systematically perform experiments, to enable their reproducibility and allow
other researchers to replicate the study and the obtained conclusions on
different infrastructures. This breaks down to the tedious process of
reconciling the numerous experimental requirements and constraints with
low-level infrastructure design choices.To address the limitations of the main
state-of-the-art approaches for distributed, collaborative experimentation,
such as Google Colab, Kaggle, and Code Ocean, we propose KheOps, a
collaborative environment specifically designed to enable cost-effective
reproducibility and replicability of Edge-to-Cloud experiments. KheOps is
composed of three core elements: (1) an experiment repository; (2) a notebook
environment; and (3) a multi-platform experiment methodology.We illustrate
KheOps with a real-life Edge-to-Cloud application. The evaluations explore the
point of view of the authors of an experiment described in an article (who aim
to make their experiments reproducible) and the perspective of their readers
(who aim to replicate the experiment). The results show how KheOps helps
authors to systematically perform repeatable and reproducible experiments on
the Grid5000 + FIT IoT LAB testbeds. Furthermore, KheOps helps readers to
cost-effectively replicate authors experiments in different infrastructures
such as Chameleon Cloud + CHI@Edge testbeds, and obtain the same conclusions
with high accuracies (> 88% for all performance metrics).
