论文标题
用于大规模云应用的简单有效的预测资源缩放启发式
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
论文作者
论文摘要
我们为在云环境中运行的水平可扩展应用程序的预测自动缩放提出了一个简单而有效的策略，在云环境中，只能延迟添加计算资源，而部署吞吐量受到限制。我们的策略使用工作量的概率预测来制定规模决策，取决于应用程序所有者的风险规避。我们在使用现实世界和合成数据的实验中显示，本策略可以比较数学上更复杂的方法以及简单的基准策略。
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be added with a delay, and where the deployment throughput is limited. Our policy uses a probabilistic forecast of the workload to make scaling decisions dependent on the risk aversion of the application owner. We show in our experiments using real-world and synthetic data that this policy compares favorably to mathematically more sophisticated approaches as well as to simple benchmark policies.
