Forecasting on different levels of the management system of a cloud data center has received increased attention due to its significant impact on the cloud services quality. Making accurate forecasts, however, is challenging due to the non-stationary workload and intrinsic complexity of the management system of a cloud data center. It is possible to prevent excessive resource allocation and service level agreement violations through workload forecasting for virtual machines and containers. In this paper, the authors propose the adaptive forecasting model and corresponding adaptive forecasting methods to apply in the management system of a cloud data center for workload forecasting, ensuring compliance with the service level agreement and power consumption decrease. The authors consider six alternative forecasting methods and 77 training data windows on each management step to determine the best combination of methods and the training set size that generates a most accurate forecast, thereby adapting to the current state of the physical or virtual server in a cloud data center. Through the comprehensive analysis, the authors also evaluate the proposed adaptive forecasting methods using real-world workload traces Bitbrains and demonstrate that combined forecasting methods outperform the individual forecasting methods significantly in terms of forecasting accuracy measured by Mean Absolute Percentage Error.

中文翻译：

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云数据中心中的自适应工作量预测

由于云数据中心对云服务质量的重大影响，因此对云数据中心管理系统不同级别的预测已受到越来越多的关注。但是，由于云数据中心管理系统的非固定工作量和固有的复杂性，因此进行准确的预测非常具有挑战性。通过对虚拟机和容器的工作负荷预测，可以防止过多的资源分配和违反服务级别协议的行为。在本文中，作者提出了一种自适应预测模型和相应的自适应预测方法，以在云数据中心的管理系统中进行工作量预测，以确保符合服务水平协议并降低功耗。作者考虑了六个备选预测方法和每个管理步骤上的77个训练数据窗口，以确定方法的最佳组合以及可以生成最准确预测的训练集大小，从而适应云中物理或虚拟服务器的当前状态。数据中心。通过全面的分析，作者还评估了使用实际工作量跟踪Bitbrains提出的自适应预测方法，并证明在通过平均绝对百分比误差衡量的预测准确性方面，组合的预测方法明显优于单个预测方法。从而适应云数据中心中物理或虚拟服务器的当前状态。通过全面的分析，作者还评估了使用实际工作量跟踪Bitbrains提出的自适应预测方法，并证明在通过平均绝对百分比误差衡量的预测准确性方面，组合的预测方法明显优于单个预测方法。从而适应云数据中心中物理或虚拟服务器的当前状态。通过全面的分析，作者还评估了使用实际工作量跟踪Bitbrains提出的自适应预测方法，并证明在通过平均绝对百分比误差衡量的预测准确性方面，组合的预测方法明显优于单个预测方法。