Analysis of Software Aging in a Database Environment
Keywords:
software aging, PostgreSQL, database, statistical analysisAbstract
Computer systems that run for long periods of time can suffer from a phenomenon known as software aging. Just like people, software can age. This phenomenon, however, can be viewed as a problem in computer systems because it can accelerate the depletion of resources and even lead to system failures. Databases are widely used software nowadays and can be affected by such a phenomenon, since they need to run for long periods of time uninterruptedly. Therefore, studies that investigate the possible effects of software aging in database environments are very necessary. In this work, we experimentally investigate the software aging phenomena in a database environment using PostgreSQL as the DBMS (Database Management System). By performing statistical analysis on the measurement data, we detected a suspicious phenomenon of software aging induced by workloads in memory and CPU usage. Additionally, our process analysis identified suspicious processes that can lead to memory degradations.
Downloads
References
DB-Engines, “DB-Engines Ranking,” https://db-engines.com/en/
ranking, 2022, [Online; accessed 11-nov-2022].
U. F. Minhas, S. Rajagopalan, B. Cully, A. Aboulnaga, K. Salem,
and A. Warfield, “Remusdb: Transparent high availability for database
systems,” The VLDB Journal, vol. 22, no. 1, pp. 29–45, 2013.
H. He, “Tuning backfired? not (always) your fault: Understanding and
detecting configuration-related performance bugs,” in Proceedings of
the 2019 27th ACM Joint Meeting on European Software Engineering
Conference and Symposium on the Foundations of Software Engineering,
, pp. 1229–1231.
D. L. Parnas, “Software aging,” in Proceedings of 16th International
Conference on Software Engineering. IEEE, 1994, pp. 279–287.
M. Grottke, R. Matias, and K. S. Trivedi, “The fundamentals of software
aging,” in 2008 IEEE International conference on software reliability
engineering workshops (ISSRE Wksp). Ieee, 2008, pp. 1–6.
A. Bovenzi, D. Cotroneo, R. Pietrantuono, and S. Russo, “On the aging
effects due to concurrency bugs: A case study on mysql,” in 2012 IEEE
rd International Symposium on Software Reliability Engineering.
IEEE, 2012, pp. 211–220.
H. B. Mann, “Nonparametric tests against trend,” Econometrica: Journal
of the econometric society, pp. 245–259, 1945.
M. G. Kendall, “Rank correlation methods.” 1948.
E. Andrade, F. Machida, R. Pietrantuono, and D. Cotroneo, “Memory
degradation analysis in private and public cloud environments,” in 2021
IEEE International Symposium on Software Reliability Engineering
Workshops (ISSREW). IEEE, 2021, pp. 33–39.
E. Andrade, F. Machida, R. Pietrantuono, and D. Cotroneo, “Software aging in image classification systems on cloud and edge,” in 2020 IEEE International Symposium on Software Reliability
Engineering Workshops (ISSREW). IEEE, 2020, pp. 342–348.
D. Dias and E. Andrade, “Análise de envelhecimento de software em
uma plataforma de blockchain,” in Anais do V Workshop em Blockchain:
Teoria, Tecnologias e Aplicações. SBC, 2022, pp. 40–53.
C. Melo, F. Oliveira, J. Dantas, J. Araujo, P. Pereira, R. Maciel, and
P. Maciel, “Performance and availability evaluation of the blockchain
platform hyperledger fabric,” The Journal of Supercomputing, pp. 1–23,
L. Vinícius, L. Rodrigues, M. Torquato, and F. A. Silva, “Docker
platform aging: a systematic performance evaluation and prediction of
resource consumption,” The Journal of Supercomputing, pp. 1–31, 2022.
F. Oliveira, J. Araujo, R. Matos, and P. Maciel, “Software aging
in container-based virtualization: an experimental analysis on docker
platform,” in 2021 16th Iberian Conference on Information Systems and
Technologies (CISTI). IEEE, 2021, pp. 1–7.
S. Huo, D. Zhao, X. Liu, J. Xiang, Y. Zhong, and H. Yu, “Using machine
learning for software aging detection in android system,” in 2018
Tenth International Conference on Advanced Computational Intelligence
(ICACI). IEEE, 2018, pp. 741–746.
Y. Qiao, Z. Zheng, and F. Qin, “An empirical study of software aging
manifestations in android,” in 2016 IEEE international symposium on
software reliability engineering workshops (ISSREW). IEEE, 2016, pp.
–90.
H. Couto, F. Silva, G. Callou, and E. Andrade, “Uma abordagem
experimental para avaliar o desempenho do banco de dados open-source
postgresql,” in Anais da X Escola Regional de Informática de Goiás.
SBC, 2022, pp. 12–23.
A. JMeter™, “Apache JMeter - What can I do with it?” https://jmeter.
apache.org/, 2022, [Online; accessed 11-nov-2022].
