基于AIOPs的IT环境下系统稳定性预测预警

Pralhad P. Teggi, Harivinod N., Bharathi Malakreddy
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引用次数: 1

摘要

许多行业和组织正在从遗留系统转向数字化转换,以优化其业务流程。IT运营人工智能(AIOps)在数字化转型中发挥着关键作用。AIOps平台利用大量的数据,结合经典的机器学习和尖端的分析技术。这将通过主动的动态活动促进IT操作。微焦点操作桥(OpsBridge)监控整个IT环境中基础设施和应用程序系统的运行状况和性能,并向各自的团队发送数百个警报。这些大量的警报产生了一种警报噪音。本文提出了一种基于AIOps的自动预测报警系统,利用逻辑回归对系统环境进行监控,降低报警噪声。这种预测性警报将识别操作数据中的异常,并对这些可能影响应用程序或服务的异常发出警报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AIOPs based Predictive Alerting for System Stability in IT Environment
Many industries and organizations are moving away from legacy systems towards digital transformation to optimize their business processes. Artificial intelligence for IT operations (AIOps) plays a pivotal role in digital transformation. AIOps platforms utilize a large amount of data coupled with classical machine learning and cutting-edge analytic technologies. This will boost IT operations with proactive dynamic activities. The Micro Focus Operations Bridge (OpsBridge) monitors the health and performance of the systems in the infrastructure and applications across their IT environment and the hundreds of alerts are delivered to respective teams. These huge number of alerts create an alert noise. In this paper, we present an AIOps based automated predictive alerting system using logistic regression to monitor the system environment and reduce the alert noise. This predictive alerting will identify abnormalities in operational data and raise an alert on these abnormalities that could potentially impact an application or service.
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