Online Memory Leak Detection in the Cloud-based Infrastructures

TL;DR

This paper introduces Precog, a machine learning algorithm that detects memory leaks in cloud applications using only system memory utilization data, achieving high accuracy without internal application knowledge.

Contribution

The paper presents a novel online memory leak detection method for cloud infrastructures that requires no internal application details, relying solely on memory utilization metrics.

Findings

Achieves 85% detection accuracy on real-world data

Operates with less than half a second prediction time per VM

Validated on 60 virtual machines with industry-provided data

Abstract

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.