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Upcoming AI-based and 5G applications are demanding new network management approaches that are capable to cope with unprecedented levels of flexibility, scalability and energy efficiency. In order to make these use cases tangible and feasible, network management solutions aim to rely on multi-domain, multi-tier architectures that permit complex end-to-end orchestration of network resources. However, current research on scheduling functions and task-offloading algorithms often focus on one single-domain, and the exploration of large-scale inter-operable solutions becomes a challenge. Fortunately for the networking research community, a number of available testing facilities deployed at different geographical location along the world can be integrated to be used as a single joint multi-domain infrastructure. In this demo paper, we present a hands-off experience of how to integrate different high-performance testbeds, located in USA, Belgium and The Netherlands, in order to enable multi-domain large-scale experimentation. We demonstrate end-to-end performance characteristics of the testbed integration and we describe the main takeaways and lessons learned to drive researchers towards successful deployments in such end-to-end global infrastructure. 

The Chameleon project developed a unique experimental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demonstrated 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. 

FABRIC is a unique national research infrastructure to enable cutting-edge andexploratory research at-scale in networking, cybersecurity, distributed computing andstorage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elementsequipped with large amounts of compute and storage, interconnected by high speed,dedicated optical links. It will connect a number of specialized testbeds for cloudresearch (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5Gtechnologies (Platforms for Advanced Wireless Research or PAWR), as well as productionhigh-performance computing facilities and science instruments to create a rich fabric fora wide variety of experimental activities. 

A majority of today's cloud services are independently operated by individual cloud service providers. In this approach, the locations of cloud resources are strictly constrained by the distribution of cloud service providers' sites. As the popularity and scale of cloud services increase, we believe this traditional paradigm is about to change toward further federated services, a.k.a., multi-cloud, due to the improved performance, reduced cost of compute, storage and network resources, as well as increased user demands. In this paper, we present COMET, a lightweight, distributed storage system for managing metadata on large scale, federated cloud infrastructure providers, end users, and their applications (e.g. HTCondor Cluster or Hadoop Cluster). We showcase use case from NSF's, Chameleon, ExoGENI and JetStream research cloud testbeds to show the effectiveness of COMET design and deployment. 

Recent advancements have expanded Chameleon’s support for networking experiments by enabling deeply pro- grammable networks spanning wide-areas and controlled by the user. New capabilities include: 1) bring-your-own-controller (BYOC) software defined networking (SDN) and 2) Layer 2 stitching to external testbeds and facilities including stitching between the two Chameleon sites. This paper presents the new networking capabilities of Chameleon along with corresponding experiments that evaluate limitations and features of using SDN in a wide-area environment. The experiments serve both as an evaluation of SDN in a wide-area environment and as a guide for designing advanced networking experiments on Chameleon. 

Recent advancements have expanded Chameleon’s support for networking experiments by enabling deeply pro- grammable networks spanning wide-areas and controlled by the user. New capabilities include: 1) bring-your-own-controller (BYOC) software defined networking (SDN) and 2) Layer 2 stitching to external testbeds and facilities including stitching between the two Chameleon sites. This paper presents the new networking capabilities of Chameleon along with corresponding experiments that evaluate limitations and features of using SDN in a wide-area environment. The experiments serve both as an evaluation of SDN in a wide-area environment and as a guide for designing advanced networking experiments on Chameleon. 

Although Software-Defined Wide Area Networks (SD-WANs) are now widely deployed in several production networks, they are largely restricted to traffic engineering ap- proaches based on layer 4 (L4) of the network protocol stack. Such approaches result in improved Quality-of-Service (QoS) of the network overall without necessarily focussing on the requirements of a specific application. However, the emergence of application protocols such as QUIC and HTTP/2 needs an in- vestigation of layer 5-based (L5) approaches in order to improve users’ Quality-of-Experience (QoE). In this paper, we leverage the capabilities of flexible, P4-based switches that incorporate protocol-independent packet processing in order to intelligently route traffic based on application headers. We use Adaptive Bit Rate (ABR) video streaming as an example to show how such an approach can not only provide flexible traffic management but also improve application QoE. Our evaluation consists of an actual deployment in a research testbed, Chameleon, where we leverage the benefits of fast paths in order to retransmit video segments in higher qualities. Further, we analyze real-world ABR streaming sessions from a large-scale CDN and show that our approach can successfully maximize QoE for all users in the dataset.