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MLscale: A machine learning based application-agnostic autoscaler

机译：MLSCALE：基于机器的应用程序 - 不可知论式自动播放器

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摘要

Autoscaling is the practice of automatically adding or removing resources for an application deployment to meet performance targets in response to changing workload conditions. However, existing autoscaling approaches typically require expert application and system knowledge to reduce resource costs and performance target violations, thus limiting their applicability. We present MLscale, an application-agnostic, machine learning based autoscaler that is composed of: (i) a neural network based online (black-box) performance modeler, and (ii) a regression based metrics predictor to estimate post-scaling application and system metrics. Implementation results for diverse applications across several traces highlight MLscale's application-agnostic behavior and show that MLscale (i) reduces resource costs by about 41%, on average, compared to the optimal static policy, (ii) is within 14%, on average, of the cost of the optimal dynamic policy, and (iii) provides similar cost-performance tradeoffs, without requiring any tuning, when compared to carefully tuned threshold-based policies. (C) 2017 Elsevier Inc. All rights reserved.
机译：AutoScaling是自动添加或删除应用程序部署的资源以满足性能目标，以响应更改工作负载条件。然而，现有的自动播放方法通常需要专家应用程序和系统知识来降低资源成本和性能目标违规行为，从而限制了他们的适用性。我们展示了MLSCale，一个应用程序无关，基于机器学习的自动阶段，它由以下组成：（i）基于在线（黑盒）性能建模器的神经网络，以及（ii）基于回归的度量预测器来估计缩放后的应用程序和系统指标。几个迹线的不同应用的实现结果突出了MLSCale的应用程序无关行为，并显示MLSCale（i）将资源成本降低约41％，平均而言，与最佳静态政策相比，（ii）在14％以内，平均而言，最佳动态政策的成本，（iii）提供了类似的成本性能权衡，而无需任何调整，与仔细调整的基于阈值的策略相比。 （c）2017年Elsevier Inc.保留所有权利。

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