Cloud service providers should be able to predict the future states of their infrastructure in order to avoid any violation of Service Level Agreement. This becomes more complex when vendors have to deal with services from various providers in multi-clouds. As a result, QoS prediction can significantly support service providers in a better understanding of their resources future states. Users should also be very well aware of their resource needs, as well as the Quality of Service relative values. This paper proposes a hybrid approach to the prediction of the future value of the QoS features. The hybrid approach uses a modified version of k-medoids algorithm for the clustering of large time-series datasets, as well as a proposed algorithm inspired from the lazy learning and lower bound Dynamic Time Warping (LB-Keogh) for pruned DTW computations. The proposed method in this manuscript is a shape-based QoS prediction with a novel pre-processing method, which fulfills the missing data with statistically semi-real data. In order to solve the cold start problem, we proposed new algorithm based on the DTW Barycenter Averaging (DBA) algorithm. The results showed that our predicted values are very close to real values and achieve only 0.35 of the normalized mean absolute error rate, on average, for the WSDream dataset and 0.07 for the Alibaba dataset.

中文翻译：

---

多云中增强的时间感知 QoS 预测：混合 k-medoids 和惰性学习方法 (QoPC)

云服务提供商应该能够预测其基础架构的未来状态，以避免违反服务级别协议。当供应商必须在多云中处理来自不同供应商的服务时，这变得更加复杂。因此，QoS 预测可以极大地支持服务提供商更好地了解其资源的未来状态。用户还应该非常清楚他们的资源需求以及服务质量的相对值。本文提出了一种混合方法来预测 QoS 特征的未来价值。混合方法使用 k-medoids 算法的修改版本来聚类大型时间序列数据集，以及受惰性学习和下限动态时间扭曲 (LB-Keogh) 启发的拟议算法，用于修剪 DTW 计算。本手稿中提出的方法是一种基于形状的 QoS 预测，采用一种新颖的预处理方法，用统计半真实数据来填补缺失数据。为了解决冷启动问题，我们提出了基于DTW重心平均（DBA）算法的新算法。结果表明，我们的预测值非常接近真实值，WSDream 数据集的平均绝对错误率仅为 0.35，而阿里巴巴数据集的平均绝对错误率仅为 0.07。