Likely system invariants model properties that hold in operating conditions of a computing system. Invariants may be mined offline from training datasets, or inferred during execution. Scientific work has shown that invariants’ mining techniques support several activities, including capacity planning and detection of failures, anomalies and violations of Service Level Agreements. However their practical application by operation engineers is still a challenge. We aim to fill this gap through an empirical analysis of three major techniques for mining invariants in cloud-based utility computing systems: clustering, association rules, and decision list. The experiments use independent datasets from real-world systems: a Google cluster, whose traces are publicly available, and a Software-as-a-Service platform used by various companies worldwide. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. A sensitivity analysis is performed. Experimental results allow inferring practical usage implications, showing that relatively few invariants characterize the majority of operating conditions, that precision and recall may drop significantly when trying to achieve a large coverage, and that techniques exhibit similar precision, though the supervised one a higher recall. Finally, we propose a general heuristic for selecting likely invariants from a dataset.

中文翻译：

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评估基于云的公用事业计算系统的不变挖掘技术

可能的系统不变量对在计算系统的操作条件下保持的属性进行建模。不变量可以从训练数据集中离线挖掘，或者在执行过程中推断出来。科学工作表明，不变量的挖掘技术支持多种活动，包括容量规划和故障、异常和违反服务水平协议的检测。然而，操作工程师的实际应用仍然是一个挑战。我们旨在通过对基于云的效用计算系统中挖掘不变量的三种主要技术的实证分析来填补这一空白：聚类、关联规则和决策列表。实验使用来自真实世界系统的独立数据集：一个谷歌集群，其跟踪是公开可用的，以及一个全球多家公司使用的软件即服务平台。我们使用覆盖率、召回率和精确度的度量来评估两个不变量应用程序中的技术，即执行表征和异常检测。进行敏感性分析。实验结果允许推断实际使用的含义，表明相对较少的不变量表征了大多数操作条件，当试图实现大覆盖时，精度和召回率可能会显着下降，并且该技术表现出相似的精度，尽管受监督的具有更高的召回率。最后，我们提出了一种从数据集中选择可能的不变量的通用启发式方法。实验结果允许推断实际使用的含义，表明相对较少的不变量表征了大多数操作条件，当试图实现大覆盖时，精度和召回率可能会显着下降，并且该技术表现出相似的精度，尽管受监督的具有更高的召回率。最后，我们提出了一种从数据集中选择可能的不变量的通用启发式方法。实验结果允许推断实际使用的含义，表明相对较少的不变量表征了大多数操作条件，当试图实现大覆盖时，精度和召回率可能会显着下降，并且该技术表现出相似的精度，尽管受监督的具有更高的召回率。最后，我们提出了一种从数据集中选择可能的不变量的通用启发式方法。