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A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

机译：一种云计算短期主机利用预测的混合方法

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摘要

Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.
机译：动态资源调度是保证云计算中服务质量（QoS）的关键活动。一个具有挑战性的问题是如何实时预测未来的主机利用。通过预测未来的主机利用率，云数据中心可以将虚拟机放置到合适的主机或预先从过载或未载荷的主机提前迁移虚拟机以保证QoS或节省能量。然而，很难以及时准确地预测主机利用，因为主机利用率很快变化并且具有许多突发的强大不稳定性。虽然机器学习方法可以准确地预测主机利用率，但它通常需要太多时间来确保快速资源分配和调度。在本文中，我们提出了一种混合方法，EEMD-RT-ARIMA，用于基于集合经验模式分解（EEMD）的短期主机利用预测，运行测试（RT）和自回归综合移动平均线（ARIMA）。首先，EEMD方法用于将非稳定主机利用率序列分解成相对稳定的内部模式功能（IMF）分量和残差分量，以提高预测精度。然后，选择高效的IMF组件，然后重建为三个新组件，以减少由于太多IMF组件引起的预测时间和误差累积。最后，通过叠加三个新部件的预测结果来获得整体预测结果，每个预测结果由ARIMA方法预测。从云平台进行真实主机利用率痕迹进行实验。我们在误差，有效性和时间成本分析方面将我们的方法与Arima模型和EEMD-Arima方法进行比较。结果表明，我们的方法是一种成本效益的方法，更适合于云计算中的短期主机利用预测。

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  • 来源
    《Journal of electrical and computer engineering》 |2019年第1期|2782349.1-2782349.14|共14页
  • 作者

    Chen Jing; Wang Yinglong;

  • 作者单位

    Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China|Qilu Univ Technol Shandong Acad Sci Shandong Prov Key Lab Comp Networks Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan 250014 Shandong Peoples R China;

    Qilu Univ Technol Shandong Acad Sci Shandong Prov Key Lab Comp Networks Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan 250014 Shandong Peoples R China;

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