IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and Big Data. However, past AIOps contributions are scattered, unorganized and missing a common terminology convention, which renders their discovery and comparison impractical. In this work, we conduct an in-depth mapping study to collect and organize the numerous scattered contributions to AIOps in a unique reference index. We create an AIOps taxonomy to build a foundation for future contributions and allow an efficient comparison of AIOps papers treating similar problems. We investigate temporal trends and classify AIOps contributions based on the choice of algorithms, data sources and the target components. Our results show a recent and growing interest towards AIOps, specifically to those contributions treating failure-related tasks (62%), such as anomaly detection and root cause analysis.
翻译:今天的信息技术系统日益扩大和复杂,使其人文监督更加困难。由于AI和Big Data,信息技术业务人工情报(AIIPs)被提议应对现代信息技术管理的挑战。然而,过去AIOPs的贡献是分散的,没有组织的,缺少一个共同的术语公约,这使得发现和比较这些贡献不切实际。在这项工作中,我们进行了深入的绘图研究,以一个独特的参考索引来收集和组织对AIOPs的众多分散贡献。我们创建了AIOPs分类,以便为未来的贡献奠定基础,并能够有效地比较处理类似问题的AIOps文件。我们调查时间趋势,并根据算法、数据来源和目标组成部分的选择对AIOps的贡献进行分类。我们的结果显示,最近对AIOps的兴趣越来越大,特别是对处理与故障有关的任务(62%)的贡献,例如异常探测和根本原因分析。