Identifying root causes for unexpected or undesirable behavior in complex
systems is a prevalent challenge. This issue becomes especially crucial in
modern cloud applications that employ numerous microservices. Although the
machine learning and systems research communities have proposed various
techniques to tackle this problem, there is currently a lack of standardized
datasets for quantitative benchmarking. Consequently, research groups are
compelled to create their own datasets for experimentation. This paper
introduces a dataset specifically designed for evaluating root cause analyses
in microservice-based applications. The dataset encompasses latency, requests,
and availability metrics emitted in 5-minute intervals from a distributed
application. In addition to normal operation metrics, the dataset includes 68
injected performance issues, which increase latency and reduce availability
throughout the system. We showcase how this dataset can be used to evaluate the
accuracy of a variety of methods spanning different causal and non-causal
characterisations of the root cause analysis problem. We hope the new dataset,
available at this https URL
enables further development of techniques in this important area.

该论文介绍了一个专门设计用于评估微服务应用中根本原因分析的数据集，该数据集涵盖了分布式应用程序在 5 分钟间隔内发出的延迟、请求和可用性指标。此外，数据集还包括 68 个性能问题，这些问题通过增加延迟和降低系统的可用性来减少。我们展示了如何使用该数据集评估涵盖了不同因果和非因果特征的各种方法的准确性。我们希望该可在 https URL 上获取的新数据集能够进一步发展该重要领域中的技术。

PetShop 数据集 — 跨微服务找出性能问题的原因

The PetShop Dataset -- Finding Causes of Performance Issues across  Microservices

High-speed research networks are built to meet the ever-increasing needs of
data-intensive distributed workflows. However, data transfers in these networks
often fail to attain the promised transfer rates for several reasons, including
I/O and network interference, server misconfigurations, and network anomalies.
Although understanding the root causes of performance issues is critical to
mitigating them and increasing the utilization of expensive network
infrastructures, there is currently no available mechanism to monitor data
transfers in these networks. In this paper, we present a scalable, end-to-end
monitoring framework to gather and store key performance metrics for file
transfers to shed light on the performance of transfers. The evaluation results
show that the proposed framework can monitor up to 400 transfers per host and
more than 40, 000 transfers in total while collecting performance statistics at
one-second precision. We also introduce a heuristic method to automatically
process the gathered performance metrics and identify the root causes of
performance anomalies with an F-score of 87 - 98%.

构建用于满足数据密集型分布式工作流需求的高速研究网络，但是由于多种原因，包括 I/O 和网络干扰、服务器配置错误和网络异常，这些网络中的数据传输经常无法达到承诺的传输速率。本文提出了一个可扩展的端到端监测框架，用于收集和存储文件传输的关键性能指标，以明确传输性能。评估结果表明，该提议的框架可以监测每个主机多达 400 个传输和总共超过 40,000 个传输，同时以一秒的精度收集性能统计数据。我们还引入了一种启发式方法，能够自动处理收集到的性能指标，并以 87-98% 的 F-score 识别性能异常的根本原因。

高性能研究网络数据传输性能的探究

Demystifying the Performance of Data Transfers in High-Performance  Research Networks

Performance issues permeate large-scale cloud service systems, which can lead
to huge revenue losses. To ensure reliable performance, it's essential to
accurately identify and localize these issues using service monitoring metrics.
Given the complexity and scale of modern cloud systems, this task can be
challenging and may require extensive expertise and resources beyond the
capacity of individual humans. Some existing methods tackle this problem by
analyzing each metric independently to detect anomalies. However, this could
incur overwhelming alert storms that are difficult for engineers to diagnose
manually. To pursue better performance, not only the temporal patterns of
metrics but also the correlation between metrics (i.e., relational patterns)
should be considered, which can be formulated as a multivariate metrics anomaly
detection problem. However, most of the studies fall short of extracting these
two types of features explicitly. Moreover, there exist some unlabeled
anomalies mixed in the training data, which may hinder the detection
performance. To address these limitations, we propose the Relational- Temporal
Anomaly Detection Model (RTAnomaly) that combines the relational and temporal
information of metrics. RTAnomaly employs a graph attention layer to learn the
dependencies among metrics, which will further help pinpoint the anomalous
metrics that may cause the anomaly effectively. In addition, we exploit the
concept of positive unlabeled learning to address the issue of potential
anomalies in the training data. To evaluate our method, we conduct experiments
on a public dataset and two industrial datasets. RTAnomaly outperforms all the
baseline models by achieving an average F1 score of 0.929 and Hit@3 of 0.920,
demonstrating its superiority.

该研究论文提出了一种关联时间异常检测模型（RTAnomaly），该模型结合了指标的关联和时间信息，通过图注意力层学习指标之间的依赖关系，并利用正无标签学习来解决训练数据中潜在异常的问题。实验证明，RTAnomaly 在公共数据集和两个工业数据集上表现优于基准模型，平均 F1 得分为 0.929，Hit@3 为 0.920。