The increasing demand to provide new network services in a timely and efficient manner is driving the need to design, test and deploy networks quickly and consistently. Testing and verifying at scale is a challenge: network equipment is expensive, requires space, power and cooling, and there is never enough test equipment for everyone who wants to use it! Network virtualization technologies enable a flexible environment for educators, researchers, and operators to create functional models of current, planned, or theoretical networks. This demonstration will show VIRL --- the Virtual Internet Routing Lab --- a platform that can be used for network change validation, training, education, research, or network-aware applications development. The platform combines network virtualization technologies with virtual machines (VMs) running open-source and commercial operating systems; VM orchestration capabilities; a context-aware configuration engine; and an extensible data-collection framework. The system simplifies the process to create both simple and complex environments, run simulations, and collect measurement data.

Emulated networks and systems, where router and server software are run in virtual environments, allow network operators and researchers to perform experiments at large scale more economically than in testbeds. Running real code provides a greater level of realism than simulation.

Emulated networks enable educators, researchers, and operators to conduct realistic network scenarios on commodity hardware. However each network device must be configured, typically in a low-level syntax. This time-consuming and error-prone process limits scalability and discourages repeated experimentation.

We present a methodology that brings simplicity to large and complex test labs by using abstraction. The networking community has appreciated the value of large scale test labs to explore complex network interactions, as seen in projects such as PlanetLab, GENI, DETER, Emulab, and SecSI. Virtualization has enabled the creation of many more such labs. However, one problem remains: it is time consuming, tedious and error prone to setup and configure large scale test networks. Separate devices need to be configured in a coordinated way, even in a virtual lab.

We present PaDIS Emulator, a fully automated platform to evaluate CDN-ISP collaboration for better content delivery, traffic engineering, and cost reduction. The PaDIS Emulator enables researchers as well as CDN and ISP operators to evaluate the benefits of collaboration using their own operational networks, configuration, and cost functions.

Network design, as it is currently practiced, involves putting devices together to create a network. However, a network is more than the sum of its parts, both in terms of the services it provides, and the potential for bugs. Devices are important, but their combination into a network should follow from expression of high-level policy, not the minutiae of network device configuration. Ideally we want to consider the network as a whole object. In this paper we develop generalized graph products that allow the mathematical design of a network in terms of small subgraphs that directly express business policy. The result is a flexible algebraic description of networks suitable for manipulation and proof. The approach is more than just design - it allows for analysis of existing networks providing an understanding of the policies used in their construction, something which can be difficult if the original designers no longer work on that network. We apply the approach to several real world networks to demonstrate how it can provide insight, and improve design.

The study of network topology has attracted a great deal of attention in the last decade, but has been hampered by a lack of accurate data. Existing methods for measuring topology have flaws, and arguments about the importance of these have overshadowed the more interesting questions about network structure. The Internet Topology Zoo is a store of network data created from the information that network operators make public. As such it is the most accurate large-scale collection of network topologies available, and includes meta-data that couldn't have been measured. With this data we can answer questions about network structure with more certainty than ever before - we illustrate its power through a preliminary analysis of the PoP-level topology of over 140 networks. We find a wide range of network designs not conforming as a whole to any obvious model.