Introducing configuration management capabilities into CloudLab experiments

Duplyakin, Dmitry; Ricci, Robert

doi:10.1109/infcomw.2016.7562042

Cited by 6 publications

(1 citation statement)

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“…RINA research is experiment-intensive and typically makes use of GENI or FIRE [14]. The generation of large experiment topologies and their configuration is very time consuming, so it is important to have tools to automate these processes [15] [16]. Our experience with manual configuration and setup of even moderately sized RINA networks showed it is an error-prone, hard to reproduce process, that can also introduce unwanted dependencies on on-premise hardware.…”

Section: Introductionmentioning

confidence: 99%

Rumba: A python framework for automating large-scale Recursive Internet Experiments on GENI and FIRE+

Vrijders

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et al. 2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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A number of recent EU-funded projects have been investigating the Recursive Internet Architecture (RINA). IRATI built an initial prototype implementation, which was extended by the PRISTINE project towards technology demonstrators showing the feasibility of the architecture and demonstrating how RINA tackles security and reliability and how it can simplify network management. Currently, ARCFIRE sets out to evaluate realistic network scenarios, scaling up experiments in terms of numbers of nodes, services and running time. In this paper we present Rumba, a free open source experimentation framework developed within ARCFIRE in order to drastically reduce the time required to deploy and conduct such large experiments. Rumba is powerful yet easy to use. It provides a simple abstraction to model the RINA network as well as APIs for reserving testbed resources, installing the prototype, configuring and bootstrapping the recursive network, running the experiment scenario, collecting the results data and releasing the testbed resources. Rumba provides QEMU, jFed and emulab support to run experiments on a local machine or on various US and EU testbeds provided by GENI and FIRE+. Our experiences show that Rumba reduces the time required to configure and run large experiments using the RINA prototypes by several orders of magnitude.

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Section: Introductionmentioning

confidence: 99%