To promote the adoption of the edge paradigm, our community needs innovative approaches for geo-distributing cloud applications across multiple locations without modifying existing business logic. While recent efforts propose using external services to orchestrate REST operations and achieve geo-distribution, relying solely on resource sharing and replication has limitations in finely distributing manipulated resources. This paper introduces a novel collaboration method that extends resources across multiple instances, going beyond simple replication. Our approach employs a shard-like strategy, enabling the creation of a distributed resource with a unified state view while mitigating a synchronization overhead. The effectiveness of our mechanism is demonstrated through a proof-of-concept implemented on top of the Kubernetes ecosystem.

Despite the importance of experiment-driven research in the distributed computing community, there has been little progress in helping researchers conduct their experiments. In most cases, they have to achieve tedious and time-consuming development and instrumentation activities to deal with the specifics of testbeds and the system under study. In order to relieve researchers of the burden of those efforts, we have developedEnosLib: a Python library that takes into account best experimentation practices and leverages modern toolkits on automatic deployment and configuration systems.EnosLibhelps researchers not only in the process of developing their experimental artifacts, but also in running them over different infrastructures. To demonstrate the relevance of our library, we discuss three experimental engines built on top ofEnosLib, and used to conduct empirical studies on complex software stacks between 2016 and 2019 (database systems, communication buses and OpenStack). By introducingEnosLib, our goal is to gather academic and industrial actors of our community around a library that aggregates everyday experiment-driven research operations. A library that has been already adopted by open-source projects and members of the scientific community thanks to its ease of use and extension.