A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system's memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85\% with less than half a second prediction time per virtual machine.
翻译:在云层上安装的应用程序的内存泄漏会影响应用程序的可用性和可靠性。 因此, 迅速识别和最终解决它是非常重要的。 但是, 在云层上运行的生产环境中, 内存泄漏检测是一个挑战, 而不了解应用程序或其内部物体分配细节。 本文通过引入基于新机器学习算法Precog, 解决了在不掌握任何内部应用知识的情况下在线检测云层基础设施内内内内内存泄漏的难题。 此算法仅使用一个尺度, 即应用程序用于探测内存泄漏的系统内存利用情况。 开发的算法的准确性在60个虚拟机器上进行了测试, 由我们的工业伙伴慕尼黑研究中心提供的人工贴标签的内存利用数据 。 发现拟议的算法实现了85 的准确度, 每台虚拟机器的预测时间不到半秒。