The physical topology of a datacenter network is fundamental as it determines latency, bisection bandwidth, the location and properties of congestion, and the network's ability to tolerate failures. Yet, topology is one of the most difficult factors to control during experimentation: when using a production datacenter or a testbed, the only topology available is the one already deployed. If running on an experimenter's own equipment, rewiring may be possible but is cumbersome and of limited scale. This presents a challenge for controlled experimentation, because such experiments should ideally be run on a range of realistic topologies. To tackle this, we present our work on Topocloud, a system that enables experimenters to construct and modify network topologies in a shared public testbed. Topocloud uses hardware P4 switches to fulfill a dual role: each port can act as either a typical Ethernet MAC-learning switch or as a “virtual wire” that connects ports transparently. We demonstrate that virtual wires in Topocloud can be used to construct low latency network topologies incorporating L2 switches.

All computing infrastructure suffers from performance variability, be it bare-metal or virtualized. This phenomenon originates from many sources: some transient, such as noisy neighbors, and others more permanent but sudden, such as changes or wear in hardware, changes in the underlying hypervisor stack, or even undocumented interactions between the policies of the computing resource provider and the active workloads. Thus, performance measurements obtained on clouds, HPC facilities, and, more generally, datacenter environments are almost guaranteed to exhibit performance regimes that evolve over time, which leads to undesirable nonstationarities in application performance. In this paper, we present our analysis of performance of the bare-metal hardware available on the CloudLab testbed where we focus on quantifying the evolving performance regimes using changepoint detection. We describe our findings, backed by a dataset with nearly 6.9M benchmark results collected from over 1600 machines over a period of 2 years and 9 months. These findings yield a comprehensive characterization of real-world performance variability patterns in one computing facility, a methodology for studying such patterns on other infrastructures, and contribute to a better understanding of performance variability in general.

Empirical performance measurements of computer systems almost always exhibit variability and anomalies. Run-to-run and server-to-server variations are common for CPU, memory, disk, and network performance characteristics. In our previous work, we focused on taming performance variability for memory, disk, and network [1] and established an interactive analysis service at: https://confirm.fyi/ to help users of the CloudLab testbed [2] better plan and conduct their experiments. In this paper, we describe our analysis of CPU variability based on over 1.3M performance measurements from nearly 1,800 servers and present our initial findings.