论文标题
AIOPS中的系统映射研究
A Systematic Mapping Study in AIOps
论文作者
论文摘要
当今的IT系统变得越来越大，变得更加复杂，使他们的人类监督变得更加困难。由于AI和大数据，已提出了IT操作（AIOPS）的人工智能（AIOPS）来应对现代IT管理挑战。但是，过去的AIOPS贡献分散，无组织和缺少共同的术语公约，这使他们的发现和比较不切实际。在这项工作中，我们进行了深入的映射研究，以在独特的参考指数中收集和组织对AIOPS的众多零散贡献。我们创建了AIOPS分类法，为未来的贡献奠定了基础，并可以对处理类似问题的AIOPS论文进行有效的比较。我们根据算法，数据源和目标成分的选择来研究时间趋势并对AIOPS进行分类。我们的结果表明，最近对AIOPS的兴趣越来越大，特别是针对治疗失败相关任务的贡献（62％），例如异常检测和根本原因分析。
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.
