Jason Anderson

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.

Computational notebooks have gained much popularity 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.

Infrastructure cloud computing allows its clients to allocate on-demand resources, typically consisting of a representation of a compute node. In general however, there is a need for allocating resources other than nodes and managing them in more controlled ways than simply on demand. This paper generalizes the familiar "compute power on demand" pattern by introducing the abstraction of an allocatable resource, describing its properties, and implementation for different types of resources. We further describe architecture for a generic allocatable resource management service that can be extended to manage diverse types of resources as well as the implementation of this architecture in the OpenStack Blazar service to manage resources ranging from bare-metal compute nodes to network segments. Finally, we provide a usage analysis of this service on the Chameleon testbed and use it to illustrate the effectiveness of resource management methods as well as the need for incentives in usage arbitration.

Although Software-Defined Wide Area Networks (SD-WANs) are now widely deployed in several production networks, they are largely restricted to traffic engineering approaches 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 investigation 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.