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Anomaly detection based on Machine Learning can be a powerful tool for understanding the behavior of large, complex computer systems in the wild. The set of anomalies seen, however, can change over time: as the system evolves, is put to different uses, and encounters different workloads, both its ‘typical’ behavior and the anomalies that it encounters can change as well. This naturally raises two questions: how effective is automated anomaly detection in this setting, and how much does anomalous behavior change over time? In this paper, we examine these question for a dataset taken from a system that manages the lifecycle of servers in datacenters. We look at logs from one year of operation of a datacenter of about 500 servers. Applying state-of-the art techniques for finding anomalous events, we find that there are a ‘core’ set of anomaly patterns that persist over the entire period studied, but that in to track the evolution of the system, we must re-train the detector periodically. Working with the administrators of this system, we find that, despite these changes in patterns, they still contain actionable insights. 

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 and established an interactive analysis service at: https://confirm.fyi/ to help users of the CloudLab testbed 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. The focus of this work is on capturing hardware variability, which can make repeatable experiments more difficult and can impact conclusions; it it this important for systems researchers to understand. (We note that, though we do not study it in this work, in the cloud, multi-tenancy and resource sharing an exacerbate the problem.) Variability also inevitably impacts performance and operation of middleware and high-level applications, contributing to the straggler problems in many domains, including HPC, Big Data, and Machine Learning, and on many types of cyberinfrastructures. We analyze the data from the CloudLab servers allocated in an exclusive fashion, with no virtualization. While our analysis focuses on the testbed that aims to promote reproducible research, we believe our approach and the findings can be of value to people who manage, analyze, and utilize shared computing resources in supercomputers, clouds, and datacenters. 

Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability when running experiments in the cloud. Focusing on networks, we assess the impact of variability on cloud-based big-data workloads by gathering traces from mainstream commercial clouds and private research clouds. Our data collection consists of millions of datapoints gathered while transferring over 9 petabytes of data. We characterize the network variability present in our data and show that, even though commercial cloud providers implement mechanisms for quality-of-service enforcement, variability still occurs, and is even exacerbated by such mechanisms and service provider policies. We show how big-data workloads suffer from significant slowdowns and lack predictability and replicability, even when state-of-the-art experimentation techniques are used. We provide guidelines for practitioners to reduce the volatility of big data performance, making experiments more repeatable.