CloudInsight: Utilizing a Council of Experts to Predict Future Cloud Application Workloads
CloudInsight: Utilizing a Council of Experts to Predict Future Cloud Application Workloads
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DOI：
10.1109/cloud.2018.00013
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发表时间：
2018-07
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影响因子：
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通讯作者：
I. Kim;Wei Wang;Yanjun Qi;M. Humphrey
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文献类型：
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作者：
I. Kim;Wei Wang;Yanjun Qi;M. Humphrey
已经提出了许多预测方法来克服云上反应式自动缩放的限制。这些方法利用通常针对特定工作负载模式的工作负载预测器，并且可能无法处理其模式可能先验未知、可能随时间动态变化或可能不规则的真实云工作负载。其结果是，资源经常配置不足或过度。为了解决这个问题，我们创建了一个新的云工作负载预测框架CloudInsight，利用多个工作负载预测器的组合功能，这些预测器共同提供了一个“专家理事会”。该集成模型中的预测因子的权重是使用多类回归基于其对当前工作负载的准确性实时确定的。在真实的工作负载跟踪下，CloudInsight的准确性比最先进的预测器高出13% - 27%。它还具有用于预测未来工作负载变化（< 100 ms）和创建新的集成工作负载预测器（< 1.1秒）的低开销。
Many predictive approaches have been proposed to overcome the limitations of reactive autoscaling on clouds. These approaches leverage workload predictors that are usually targeted for a particular workload pattern and can fail to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time, or may be irregular. The result is that resources are frequently under-and overprovisioned. To address this problem, we create a novel cloud workload prediction framework called CloudInsight, leveraging the combined power of multiple workload predictors that collectively provide a "council of experts". The weights of the predictors in this ensemble model are determined in real-time based on their accuracy for current workload using multi-class regression. Under real workload traces, CloudInsight has 13% – 27% better accuracy than state-of-the-art predictors. It also has low overhead for predicting future workload changes (< 100 ms) and creating a new ensemble workload predictor (< 1.1 sec.).