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Assessing Invariant Mining Techniques for Cloud-Based Utility Computing Systems

机译：评估基于云的公用计算系统的不变挖掘技术

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摘要

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.
机译：可能的系统不变量建模在计算系统的运行条件下成立的属性。可以从训练数据集中离线挖掘不变量，或在执行过程中进行推断。科学工作表明，不变式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。但是，运维工程师的实际应用仍然是一个挑战。我们旨在通过对三种主要的基于云的公用计算系统中的不变式挖掘技术进行实证分析来填补这一空白：聚类，关联规则和决策列表。实验使用来自现实世界系统的独立数据集：一个Google集群（其跟踪记录可以公开获得）以及一个由全球多家公司使用的软件即服务平台。我们使用覆盖率，召回率和精确度的指标来评估两个不变式应用程序中的技术，即执行特征和异常检测。进行灵敏度分析。实验结果可以推断出实际使用的含义，表明大多数操作条件都具有相对较少的不变性，当尝试实现较大的覆盖范围时，精度和查全率可能会显着下降，并且尽管受监督的查全率较高，但技术仍具有相似的精度。最后，我们提出了一种从数据集中选择可能不变式的一般启发式方法。

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