论文标题
使用LSTM和Sarima型号预测CPU使用情况
Using LSTM and SARIMA Models to Forecast Cluster CPU Usage
论文作者
论文摘要
随着大型云计算中心比单个服务器变得更加流行，预测未来的资源需求已成为一个重要的问题。预测资源需求使公共云提供商可以主动分配或交易云服务。这项工作旨在在短期和长期尺度上预测一种资源，即CPU使用。 为了深入了解最能支持特定任务的模型特征，我们考虑了两个截然不同的体系结构：历史上相关的Sarima模型和更现代的神经网络LSTM模型。我们将这些模型应用于每个数据点20分钟的Azure数据，其目的是在接下来的一个小时内预测短期任务的使用情况，并在接下来的三天内完成长期任务。 Sarima模型在长期预测任务中优于LSTM，但在短期任务上表现较差。此外，LSTM模型更强大，而Sarima模型依赖于符合某些关于季节性的假设的数据。
As large scale cloud computing centers become more popular than individual servers, predicting future resource demand need has become an important problem. Forecasting resource need allows public cloud providers to proactively allocate or deallocate resources for cloud services. This work seeks to predict one resource, CPU usage, over both a short term and long term time scale. To gain insight into the model characteristics that best support specific tasks, we consider two vastly different architectures: the historically relevant SARIMA model and the more modern neural network, LSTM model. We apply these models to Azure data resampled to 20 minutes per data point with the goal of predicting usage over the next hour for the short-term task and for the next three days for the long-term task. The SARIMA model outperformed the LSTM for the long term prediction task, but performed poorer on the short term task. Furthermore, the LSTM model was more robust, whereas the SARIMA model relied on the data meeting certain assumptions about seasonality.
