AIOps for a Cloud Object Storage Service

A. Levin, Shelly Garion, E. K. Kolodner, D. Lorenz, K. Barabash, Mike Kugler, Niall McShane
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引用次数: 17

Abstract

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight. In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.
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云对象存储服务的AIOps
随着对无处不在的IT系统和服务的日益依赖,这些系统变得更加全球化、规模化和操作复杂。为了维持业务的生存能力,IT服务提供商必须提供可靠且经济高效的操作支持。人工智能用于IT运营(AIOps)是一种很有前途的技术,用于减轻IT系统和服务的操作复杂性。AIOps平台利用大数据、机器学习和其他先进的分析技术,通过主动、可操作的动态洞察来增强IT运营。在本文中,我们将分享我们将AIOps方法应用于生产云对象存储服务的经验,以获得对系统行为和健康状况的可操作见解。我们描述了一个真实的生产云规模服务及其运营数据,展示了我们创建的AIOps平台,并展示了它如何帮助我们解决运营痛点。
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