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Self-driving cars can revolutionize transportation systems, o!ering the potential to significantly enhance efficiency while also addressing the critical issue of human fatalities on roadways. Hence, there is a need to investigate methods to enhance self-driving technologies through end-to-end learning techniques. In this paper, we investigate methodologies that integrate Convolutional Neural Networks (CNNs) to enhance self-driving consistency through real-time velocity and steering estimation. We extend an end-to-end state-of-the-art learning solution with real-time speed data as additional model input to refine reliability. Specifically, our work integrates an optical encoder sensor system to record car speed during training data collection, ensuring the throttle can be regulated during model inference. An end-to-end experimental testbed is deployed on the Chameleon cloud using CHI@Edge infrastructure to manage a 1:18 scaled car, equipped with a Raspberry Pi as its onboard computer. Finally, we provide guidance that facilitates reproducibility and highlight the challenges and limitations of supporting such experiments. 

Practical reproducibility is the ability to reproduce results is a manner that is cost-effective enough to become a vehicle of mainstream scientific exploration. Since computational research artifacts usually require some form of computing to interpret, open and programmable infrastructure, such as a range of NSF-supported testbeds spanning infrastructure from datacen- ter through networks to wireless systems, is a necessary – but not sufficient – requirement for reproducibility. The question arises what other services and tools should build on the availability of such programmable infrastructure to foster the development and sharing of findable, accessible, integrated, and reusable (FAIR) experiments that underpin practical reproducibility. In this paper, we propose three such services addressing the problems of packaging for reuse, findability, and accessibility, respectively. We describe how we developed these services in Chameleon, an NSF-funded testbed for computer science research which has supported the research of a community of 8,000+ users, and discuss their strengths and limitations. 

Clouds are shareable scientific instruments that create the potential for reproducibility by ensuring that all investigators have access to a common execution platform on which computational experiments can be repeated and compared. By virtue of the interface they present, they also lead to the creation of digital artifacts compatible with the cloud, such as images or orchestration templates, that go a long way—and sometimes all the way—to representing an experiment in a digital, repeatable form. In this article, I describe how we developed these natural advantages of clouds in the Chameleon testbed and argue that we should leverage them to create a digital research marketplace that would make repeating experiments as natural and viable part of research as sharing ideas via reading papers is today. 

The Chameleon project developed a unique experi- mental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demon- strated that clouds can be configured to serve as a platform for this type research. More recently, the CloudBank project embarked on a mission of providing a conduit to commercial clouds for the systems research community that eliminates much of the complexity and some of the cost of using them for research. This creates an opportunity to explore running systems experiments in a combined setting, spanning both research and commercial clouds. In this paper, we present an extension to Chameleon for constructing controlled experiments across its resources and commercial clouds accessible via CloudBank, present a case study of an experiment running across such combined resources, and discuss the impact of using a combined research platform. 

Computational notebooks have gained much pop- ularity as a way of documenting research processes; they allow users to express research narrative by integrating ideas expressed as text, process expressed as code, and results in one executable document. However, the environments in which the code can run are currently limited, often containing only a fraction of the resources of one node, posing a barrier to many computations. In this paper, we make the case that integrating complex experimental environments, such as virtual clusters or complex networking environments that can be provisioned via infrastructure clouds, into computational notebooks will significantly broaden their reach and at the same time help realize the potential of clouds as a platform for repeatable research. To support our argument, we describe the integration of Jupyter notebooks into the Chameleon cloud testbed, which allows the user to define complex experimental environments and then assign processes to elements of this environment similarly to the way a laptop user may switch between different desktops. We evaluate our approach on an actual experiment from both the development and replication perspective. 

The Chameleon testbed is a case study in adapting the cloud paradigm for computer science research. In this paper, we explain how this adaptation was achieved, evaluate it from the perspective of supporting the most experiments for the most users, and make a case that utilizing mainstream technology in research testbeds can increase efficiency without compro- mising on functionality. We also highlight the opportunity inherent in the shared digital artifacts generated by testbeds and give an overview of the efforts we’ve made to develop it to foster reproducibility.