将Netflix应用程序上的 "内存不足 "预测作为一个机器学习问题来表述

by Aryan Mehrawith Farnaz Karimdady Sharifabad, Prasanna Vijayanathan, Chaïna Wade, Vishal Sharma and Mike Schassberger

作者 Aryan Mehra与 Farnaz Karimdady Sharifabad, Prasanna Vijayanathan, Chaïna Wade, 维沙尔-夏尔马 和 麦克-沙斯伯格

Aim and Purpose — Problem Statement

目的和宗旨 - 问题陈述

The purpose of this article is to give insights into analyzing and predicting “out of memory” or OOM kills on the Netflix App. Unlike strong compute devices, TVs and set top boxes usually have stronger memory constraints. More importantly, the low resource availability or “out of memory” scenario is one of the common reasons for crashes/kills. We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis. Specifically, if we are able to predict or analyze the Out of Memory kills, we can take device specific actions to pre-emptively lower the performance in favor of not crashing — aiming to give the user the ultimate Netflix Experience within the “performance vs pre-emptive action” tradeoff limitations. A major advantage of prediction and taking pre-emptive action, is the fact that we can take actions to better the user experience.

这篇文章的目的是对分析和预测Netflix应用程序的 "内存不足 "或OOM杀戮提出见解。与强大的计算设备不同，电视和机顶盒通常有更强的内存限制。更重要的是，资源可用性低或 "内存不足 "的情况是导致崩溃/死亡的常见原因之一。我们Netflix作为一个在数百万设备上运行的流媒体服务，在我们的大数据平台上有大量的关于设备能力/特征和运行时数据的数据。有了大量的数据，就有了利用数据进行预测和分类分析的机会。具体来说，如果我们能够预测或分析内存不足的情况，我们可以采取具体的设备行动，预先降低性能，以利于不崩溃--目的是在 "性能与预先行动 "的权衡限制下，为用户提供最终的Netflix体验。预测和采取先发制人行动的一个主要优势是，我们可以采取行动来改善用户体验。

This is done by first elaborating on the dataset curation stage — specially focussing on device capabilities and OOM kill related memory readings. We also highlight steps and guidelines for exploratory analysis and prediction to understand Out of Memory kills on a sample set of devices. Since memory management is not something one usually associates with classificat...