Large-scale network services can consist of tens of thousands of machines running thousands of unique software configurations spread across hundreds of physical networks. Testing such services for complex performance problems and configuration errors remains a difficult problem. Existing testing techniques, such as simulation or running smaller instances of a service, have limitations in predicting overall service behavior at such scales. Testing large services should ideally be done at the same scale and configuration as the target deployment, which can be technically and economically infeasible. We present DieCast , an approach to scaling network services in which we multiplex all of the nodes in a given service configuration as virtual machines across a much smaller number of physical machines in a test harness. We show how to accurately scale CPU, network, and disk to provide the illusion that each VM matches a machine in the original service in terms of both available computing resources and communication behavior. We present the architecture and evaluation of a system we built to support such experimentation and discuss its limitations. We show that for a variety of services---including a commercial high-performance cluster-based file system---and resource utilization levels, DieCast matches the behavior of the original service while using a fraction of the physical resources.

ModelNet is a network emulator designed for repeatable, large-scale experimentation with real networked systems. This talk introduces the ideas behind ModelNet that have made it a successful experimental platform. Beyond these core concepts, the talk highlights the latest additions to our methodology to test the next generation of network protocols and applications. Many of these developments address the datacenter compute environment: high-capacity networks, sophisticated infrastructure software (storage and virtualization), and complex network load. While these efforts significantly extend ModelNet's capabilities, there remain a number of open challenges, including incorporating new performance objectives (energy) and multicore architectures.

This work explores the viability and benefits of time dilation - providing the illusion to an operating system and its applications that time is passing at a rate different from real time. For example, we may wish to convince a system that for every 10 seconds of wall clock time, only one second of time passes in the host's dilated time frame. This enables external stimuli to appear to take place at higher rates than would be physically possible. For example, a host dilated by a factor of 10 receiving data from a network interface at a real rate of 1-Gbps believes it is receiving data at 10-Gbps.