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.

在动态云环境中，使用预测自动缩放来预测服务器的工作负载，并提前准备资源以确保服务水平目标（SLOs）。然而，在实践中，由于外部事件（例如销售促销活动和应用程序重新配置）导致的异常流量，其预测任务通常会遭受性能下降的影响。针对这个问题，我们提出了一种基于回放的持续学习方法，即基于密度的内存选择和基于提示的网络学习模型（DMSHM），只使用历史记录的一小部分来实现准确的预测。我们同时还展示了 DMSHM 在真实工业应用中杰出的实用性。

预测性自动缩放中的持续学习

Continual Learning in Predictive Autoscaling

Predictive autoscaling (autoscaling with workload forecasting) is an
important mechanism that supports autonomous adjustment of computing resources
in accordance with fluctuating workload demands in the Cloud. In recent works,
Reinforcement Learning (RL) has been introduced as a promising approach to
learn the resource management policies to guide the scaling actions under the
dynamic and uncertain cloud environment. However, RL methods face the following
challenges in steering predictive autoscaling, such as lack of accuracy in
decision-making, inefficient sampling and significant variability in workload
patterns that may cause policies to fail at test time. To this end, we propose
an end-to-end predictive meta model-based RL algorithm, aiming to optimally
allocate resource to maintain a stable CPU utilization level, which
incorporates a specially-designed deep periodic workload prediction model as
the input and embeds the Neural Process to guide the learning of the optimal
scaling actions over numerous application services in the Cloud. Our algorithm
not only ensures the predictability and accuracy of the scaling strategy, but
also enables the scaling decisions to adapt to the changing workloads with high
sample efficiency. Our method has achieved significant performance improvement
compared to the existing algorithms and has been deployed online at Alipay,
supporting the autoscaling of applications for the world-leading payment
platform.

本研究提出了一种基于端对端预测元模型的强化学习算法，通过神经过程的指导，将计算资源优化分配到应用程序服务中，以维持稳定的 CPU 利用水平，以应对动态变化的工作负载。该算法不仅确保了扩展策略的可预测性和准确性，还能够在高样本效率下自适应地适应不断变化的工作负载，经过阿里平台的在线部署，为世界领先的支付平台提供了应用程序的自动化扩缩容支持，达到了显著的性能提升。