Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. However, in practice, its prediction task often suffers from performance degradation under abnormal traffics caused by external events (such as sales promotional activities and applications re-configurations), for which a common solution is to re-train the model with data of a long historical period, but at the expense of high computational and storage costs. To better address this problem, we propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM), using only a small part of the historical log to achieve accurate predictions. First, we discover the phenomenon of sample overlap when applying replay-based continual learning in prediction tasks. In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set. Then we implement hint-based network learning based on hint representation to optimize the parameters. Finally, we conduct experiments on public and industrial datasets to demonstrate that our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy. Furthermore, we demonstrate remarkable practicability of DMSHM in real industrial applications.

翻译：预测性自动伸缩用于预测服务器工作负载并提前准备资源，从而在动态云环境中确保服务等级目标（SLO）。然而在实践中，其预测任务常因外部事件（如促销活动、应用重新配置）引发的异常流量导致性能下降。常规解决方案是使用长时间历史数据重新训练模型，但这会带来高昂的计算和存储成本。为更好地解决该问题，我们提出一种基于回放的持续学习方法——密度化内存选择与提示式网络学习模型（DMSHM），仅需少量历史日志即可实现精准预测。首先，我们发现将基于回放的持续学习应用于预测任务时会出现样本重叠现象。为克服该挑战并有效整合新样本分布，我们提出基于密度的样本选择策略：通过核密度估计计算样本密度作为权重参考，并采用加权采样构建新记忆集。然后基于提示表征实现提示式网络学习以优化参数。最后，在公开数据集和工业数据集上的实验表明，我们的方法在记忆容量和预测精度方面均优于现有最优持续学习方法。此外，我们证明了DMSHM在真实工业应用中的显著实用性。