云计算环境下的在线自演化异常检测
Online Self-Evolving Anomaly Detection in Cloud Computing Environments
摘要：
现代云计算系统包含数百到数千个计算和存储服务器。这样的规模，加上不断增长的系统复杂性，正在给可靠的云计算的故障和资源管理带来关键挑战。自主故障检测是理解云范围内的紧急现象和自我管理云资源以确保系统级可靠性的关键技术。为了检测故障，我们需要监视云执行并收集运行时性能数据。这些数据通常是未标记的，因此在生产云中并不总是可以获得先前的故障历史。在本文中，我们提出了一个\emph{self-evolving anomaly detection}（SEAD）的云可靠性保证框架。我们的框架通过递归地探索新验证的异常记录并不断在线更新异常检测器来自我进化。作为我们的框架的一个明显优势，云系统管理员只需要检查少量检测到的异常，他们的决定被用来更新检测器。因此，检测器随着系统硬件的升级、软件栈的更新和用户工作负载的变化而发展。此外，我们设计了两种类型的检测器，一种用于一般的异常检测和其他类型的特定异常检测。在自进化技术的帮助下，我们的检测器平均可以达到88.94%的灵敏度和94.60%的特异性，这使得它们适合于现实世界的部署。
英文摘要：
Modern cloud computing systems contain hundreds to thousands of computing and storage servers. Such a scale, combined with ever-growing system complexity, is causing a key challenge to failure and resource management for dependable cloud computing. Autonomic failure detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled, and thus a prior failure history is not always available in production clouds. In this paper, we present a \emph{self-evolving anomaly detection} (SEAD) framework for cloud dependability assurance. Our framework self-evolves by recursively exploring newly verified anomaly records and continuously updating the anomaly detector online. As a distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies, and their decisions are leveraged to update the detector. Thus, the detector evolves following the upgrade of system hardware, update of the software stack, and change of user workloads. Moreover, we design two types of detectors, one for general anomaly detection and the other for type-specific anomaly detection. With the help of self-evolving techniques, our detectors can achieve 88.94\% in sensitivity and 94.60\% in specificity on average, which makes them suitable for real-world deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
