TY - GEN
T1 - Online Memory Leak Detection in the Cloud-Based Infrastructures
AU - Jindal, Anshul
AU - Staab, Paul
AU - Cardoso, Jorge
AU - Gerndt, Michael
AU - Podolskiy, Vladimir
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Cloud
KW - Linear regression
KW - Memory leak
KW - Memory leak patterns
KW - Online memory leak detection
UR - http://www.scopus.com/inward/record.url?scp=85111398103&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76352-7_21
DO - 10.1007/978-3-030-76352-7_21
M3 - Conference contribution
AN - SCOPUS:85111398103
SN - 9783030763510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 188
EP - 200
BT - Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings
A2 - Hacid, Hakim
A2 - Outay, Fatma
A2 - Paik, Hye-young
A2 - Alloum, Amira
A2 - Petrocchi, Marinella
A2 - Bouadjenek, Mohamed Reda
A2 - Beheshti, Amin
A2 - Liu, Xumin
A2 - Maaradji, Abderrahmane
PB - Springer Science and Business Media Deutschland GmbH
T2 - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
Y2 - 14 December 2020 through 17 December 2020
ER -