The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

中文翻译：

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n使用转移学习的云应用程序企业时间序列预测系统

应用程序性能监视/管理（APM）软件的主要目的是确保应用程序的最高可用性，效率和安全性。APM软件通过自动化，测量，分析和诊断来实现主要目标。Gartner指定了APM软件的三个关键功能。首先是最终用户体验监控，用于揭示用户与应用程序和基础架构组件之间的交互。第二个是应用程序发现，诊断和跟踪。第三个关键组件是基于机器学习（ML）和人工智能（AI）的数据分析，用于预测，异常检测，事件关联和根本原因分析。时间序列指标，日志和跟踪是可观察性的三大支柱，并且是IT运营的宝贵信息来源。准确的，可扩展且健壮的时间序列预测和异常检测是分析所要求的功能。基于神经网络（NN）和深度学习的方法因其灵活性和解决复杂非线性问题的能力而越来越受欢迎。但是，基于NN的分布式云应用程序模型的某些缺点会减轻期望，并需要特定的方法。我们演示了如何使用转移学习将在全球时间序列数据库上进行预训练的NN模型应用于特定于客户的数据。通常，NN模型只能在固定时间序列上充分运行。应用于非平稳时间序列需要多层数据处理，包括对数据分类的假设检验，特定类别到平稳数据的转换，预测和向后转换。我们介绍了这种方法的数学背景，并讨论了基于VMware（APM软件）对Wavefront实施的实验结果，同时监控了实际的客户云环境。