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Distributed infrastructures for computation and analytics are now evolvingtowards an interconnected ecosystem allowing complex scientific workflows to beexecuted across hybrid systems spanning from IoT Edge devices to Clouds, andsometimes to supercomputers (the Computing Continuum). Understanding theperformance trade-offs of large-scale workflows deployed on such complexEdge-to-Cloud Continuum is challenging. To achieve this, one needs tosystematically perform experiments, to enable their reproducibility and allowother researchers to replicate the study and the obtained conclusions ondifferent infrastructures. This breaks down to the tedious process ofreconciling the numerous experimental requirements and constraints withlow-level infrastructure design choices.To address the limitations of the mainstate-of-the-art approaches for distributed, collaborative experimentation,such as Google Colab, Kaggle, and Code Ocean, we propose KheOps, acollaborative environment specifically designed to enable cost-effectivereproducibility and replicability of Edge-to-Cloud experiments. KheOps iscomposed of three core elements: (1) an experiment repository; (2) a notebookenvironment; and (3) a multi-platform experiment methodology.We illustrateKheOps with a real-life Edge-to-Cloud application. The evaluations explore thepoint of view of the authors of an experiment described in an article (who aimto make their experiments reproducible) and the perspective of their readers(who aim to replicate the experiment). The results show how KheOps helpsauthors to systematically perform repeatable and reproducible experiments onthe Grid5000 + FIT IoT LAB testbeds. Furthermore, KheOps helps readers tocost-effectively replicate authors experiments in different infrastructuressuch as Chameleon Cloud + CHI@Edge testbeds, and obtain the same conclusionswith high accuracies (> 88% for all performance metrics).

KheOps: Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments