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Whetstone: Reliable Monitoring of Cloud Services

机译：磨刀石：可靠的云服务监控

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Cloud services have become powerful enablers for a variety of smart computing solutions supporting multimedia, social networking, e-commerce and critical infrastructures among others. Consequently, as we increasingly depend on the cloud, the need exists to ensure its effective role as a trustworthy services platform. Towards this objective, a plethora of cloud monitoring mechanisms have been proposed which typically assume that the collected monitoring information is reliably correct. In reality, the information collected by cloud monitors is often susceptible to reliability issues (e.g., monitor malfunctions, data corruptions, or data tampering), and obtaining reliable cloud monitoring information is still an open issue. We propose Whetstone as a novel approach to address the gap where an efficient approach of ascertaining reliable values from a set of collected monitoring data is required. To this end, Whetstone first introduces a statistical approach to filter defective data from the collected data set. Next, Whetstone develops an optimization approach to quantify the reliability of the collected data by leveraging the value deviation of the collected data. Finally, Whetstone devises a weighted aggregation approach for generating the reliable value based on the obtained information. We evaluate the proposed approach with different experimental configurations. The experimental results demonstrate the efficacy of our approach for successfully generating the maximum likelihood reliable value for raw data sets.
机译：云服务已经成为支持各种智能计算解决方案的强大支持者，这些解决方案支持多媒体，社交网络，电子商务和关键基础架构等。因此，随着我们越来越依赖云，存在确保其作为可信赖的服务平台的有效作用的需求。为了实现这一目标，已经提出了许多云监视机制，这些机制通常假定所收集的监视信息是可靠正确的。实际上，由云监控器收集的信息通常易受可靠性问题（例如，监控器故障，数据损坏或数据篡改）的影响，而获得可靠的云监控信息仍然是一个未解决的问题。我们建议使用磨刀石作为一种新颖的方法来解决这一空白，在这种方法中，需要一种有效的方法来从一组收集的监视数据中确定可靠的值。为此，Whetstone首先引入了一种统计方法来从收集的数据集中过滤缺陷数据。接下来，Whetstone开发了一种优化方法，通过利用收集数据的值偏差来量化收集数据的可靠性。最后，Whetstone设计了一种加权聚合方法，用于基于获取的信息来生成可靠值。我们用不同的实验配置评估提出的方法。实验结果证明了我们的方法成功地为原始数据集生成了最大似然可靠值的功效。

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