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A memory leak in an application deployed on the cloud can affect theavailability and reliability of the application. Therefore, identifying andultimately resolve it quickly is highly important. However, in the productionenvironment running on the cloud, memory leak detection is a challenge withoutthe knowledge of the application or its internal object allocation details.  This paper addresses this challenge of detection of memory leaks incloud-based infrastructure without having any internal knowledge by introducingtwo novel machine learning-based algorithms: Linear Backward Regression (LBR)and Precog and, their two variants: Linear Backward Regression with ChangePoints Detection (LBRCPD) and Precog with Maximum Filteration (PrecogMF). Thesealgorithms only use one metric i.e the system's memory utilization on which theapplication is deployed for detection of a memory leak. The developedalgorithm's accuracy was tested on 60 virtual machines manually labeled memoryutilization data and it was found that the proposed PrecogMF algorithm achievesthe highest accuracy score of 85%. The same algorithm also achieves this bydecreasing the overall compute time by 80% when compared to LBR's compute time.  The paper also presents the different memory leak patterns found in thevarious memory leak applications and are further classified into differentclasses based on their visual representation.

Memory Leak Detection Algorithms in the Cloud-based Infrastructure