SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

中文翻译：

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针对云中大数据分析应用程序的SLA违规检测

违反SLA的行为确实发生在现实世界中。违反SLA表示无法保证服务质量，从而导致不良后果，例如罚款，利润减少，声誉下降，客户流失和服务中断。因此，在云托管的大数据分析应用程序（BDAA）的上下文中，提供商预测和防止违反SLA至关重要。虽然已经将基于机器学习的技术应用于检测Web服务或通用云服务的SLA违规行为，但仍缺乏有关检测专用于云托管的BDAA的SLA违规行为的研究。在本文中，我们提出了四种机器学习技术，并集成了12种重采样方法来检测云中基于批处理的BDAA的SLA违规行为。与基于真实世界跟踪数据集（阿里巴巴）的理想分类器和基准分类器相比，我们评估了提出的技术的效率。我们的工作不仅可以帮助提供商选择性能最佳的预测技术，还可以为他们提供跨层发现BDAA多种配置的隐藏模式的功能。