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.
- Memory leak
- Online memory leak detection
- Memory leak patterns
- Linear regression
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This work was supported by the funding of the German Federal Ministry of Education and Research (BMBF) in the scope of the Software Campus program. The authors also thank the anonymous reviewers whose comments helped in improving this paper.
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Jindal, A., Staab, P., Cardoso, J., Gerndt, M., Podolskiy, V. (2021). Online Memory Leak Detection in the Cloud-Based Infrastructures. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76351-0
Online ISBN: 978-3-030-76352-7