Bayesian Model-Based Prediction of Service Level Agreement Violations for Cloud Services

B. Tang, Mingdong Tang
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引用次数: 25

Abstract

Cloud SLAs are contractually binding agreements between cloud service providers and cloud consumers. For cloud service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen. We propose an approach for predicting SLA violations, which uses measured datasets (QoS of used services) as input for a prediction model. As a feature of cloud service, we consider response-time to predict violations of SLA. The prediction model is based on Naive Bayesian Classifier, and trained using historical SLA datasets. We present the basics of our prediction approach, and also determine the most effective combinations of features for prediction, and briefly validate our approach, using a detailed real SLA datasets of cloud services. Experiments result show that the Bayesian method achieves higher accuracy compared with other prediction methods.
基于贝叶斯模型的云服务服务水平协议违反预测
云sla是云服务提供商和云消费者之间的契约约束协议。对于云服务提供商来说,为了提高客户满意度和避免罚款,必须尽可能防止违反SLA。因此,提供者希望在可能的违规发生之前预测它们。我们提出了一种预测SLA违规的方法,该方法使用测量数据集(使用服务的QoS)作为预测模型的输入。作为云服务的一个特征,我们考虑响应时间来预测SLA的违反。该预测模型基于朴素贝叶斯分类器,并使用历史SLA数据集进行训练。我们介绍了预测方法的基础,并确定了最有效的预测特征组合,并使用详细的真实云服务SLA数据集简要验证了我们的方法。实验结果表明,与其他预测方法相比,贝叶斯方法具有更高的预测精度。
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