Interactive Temporal Recurrent Convolution Network for Traffic Predictionin Data Centers
Interactive Temporal Recurrent Convolution Network for Traffic Predictionin Data Centers
复制标题
用于数据中心流量预测的交互式时态循环卷积网络
DOI：
10.1109/access.2017.2787696
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhang, Weiming
中科院分区：
文献类型：
--
作者：
Cao, Xiaofeng;Zhong, Yuhua;Zhang, Weiming
准确地预测未来的服务流量将对负载平衡和资源分配有很大帮助，这在保证云计算中的服务质量（QoS）方面起着关键作用。随着数据中心的快速发展，大规模网络流量预测需要更合适的方法来处理复杂的属性（例如，高维，远距离依赖，非线性等）。但是，由于传统方法的局限性（例如，强大的理论假设和简单实施），很少有研究工作能够有效，准确地预测大型网络流量。更重要的是，大多数研究仅采用时间特征，但没有考虑服务通信，这可能会削弱数据中心的应用程序QoS。为此，我们将封闭式复发单元（GRU）模型和交互式时间重复卷积网络（ITRCN）应用于单服务流量预测和交互式网络流量预测。尤其是，ITRCN将整个服务之间的通信进行整体，并直接预测大规模网络中的交互式流量。在ITRCN模型中，卷积神经网络（CNN）部分将网络流量学习为图像以捕获网络范围的服务相关性，而GRU部分学习了时间功能，以帮助交互式网络流量预测。我们根据Yahoo！进行了全面的实验。数据集，结果表明，提出的新方法的表现分别提高了14.3％和13.0％的根平方误差，从而优于常规的GRU和CNN方法。
Accurately predicting future service traffic would be of great help for load balancing and resource allocation, which plays a key role in guaranteeing the quality of service (QoS) in cloud computing. With the rapid development of data center, the large-scale network traffic prediction requires more suitable methods to deal with the complex properties (e.g., high-dimension, long-range dependence, non-linearity, and so on). However, due to the limitations of traditional methods (e.g., strong theoretical assumptions and simple implementation), few research works could predict the large-scale network traffic efficiently and accurately. More importantly, most of the studies took only the temporal features but without the services communications into consideration, which may weaken the QoS of applications in the data center. To this end, we applied the gated recurrent unit (GRU) model and the interactive temporal recurrent convolution network (ITRCN) to single-service traffic prediction and interactive network traffic prediction, respectively. Especially, ITRCN takes the communications between services as a whole and directly predicts the interactive traffic in large-scale network. Within the ITRCN model, the convolution neural network (CNN) part learns network traffic as images to capture the network-wide services correlations, and the GRU part learns the temporal features to help the interactive network traffic prediction. We conducted comprehensive experiments based on the Yahoo! data sets, and the results show that the proposed novel method outperforms the conventional GRU and CNN method by an improvement of 14.3% and 13.0% in root mean square error, respectively.