![](bg7.png)
• The first CPA scales the Catalog microservice ,
proactively scales the Order microservice and de-
fine scaling plan for the Customer microservice.
• The second CPA scales the Order microservice
and proactively scales the Customer microservice.
According to the evaluation tests, the response
time reduction rate is up to 39%. Figure 7 shows the
application’s response time when using the HPA and
figure 8 illustrates it when using TERA-Scaler.
6 CONCLUSION
The purpose of our contribution is to propose an auto-
scaling solution for e-business applications, with the
aim of optimizing performance and reducing costs in
the cloud environment. Although our proposed solu-
tion, TERA-Scaler, is suitable for a range of applica-
tions, it is particularly well-suited for pipelines and
component-interactive systems.
Our custom auto-scaler employs a dependency-
based approach to proactively adjust resource allo-
cation to microservices in the cloud, while consider-
ing the dependencies and quality requirements of each
microservice through a multicriteria approach.
To implement this strategy, we use the CPA frame-
work to define the TERA-Scaler policy and related
functions, which are then executed on the application.
However, by leveraging the TERA-Scheduler in the
deployment environment, to ensure the deployment of
each microservice together with its dependencies on
the same node, our policy can be defined with HPA
commands and implemented without any intermedi-
ary, resulting in faster and more efficient auto-scaling.
As a future direction, we aim to integrate the ele-
ments of TERA-Solution into an orchestration tool for
microservice-based applications, taking into account
dependencies between microservices and the mainte-
nance of quality requirements for each microservice.
REFERENCES
Alexander, K., Hanif, M., Lee, C., Kim, E., and Helal, S.
(2020). Cost-aware orchestration of applications over
heterogeneous clouds. PLOS ONE, 15(2):1–21.
Bauer, A., Lesch, V., Versluis, L., Ilyushkin, A., Herbst,
N., and Kounev, S. (2019). Chamulteon: Coordinated
auto-scaling of micro-services. In 2019 IEEE 39th
International Conference on Distributed Computing
Systems (ICDCS), pages 2015–2025.
Bauer, D. A., Bauer, D. A., Penz, B., M
¨
aki
¨
o, J., and Assaad,
M. (2018). Improvement of an existing microservices
architecture for an e- learning platform. In STEM Ed-
ucation.
Bravetti, M., Giallorenzo, S., Mauro, J., Talevi, I., and Za-
vattaro, G. (2019). Optimal and automated deploy-
ment for microservices.
Carri
´
on, M. (2022). Kubernetes as a standard container or-
chestrator - a bibliometric analysis. Journal of Grid
Computing, 20.
Crankshaw, D., Sela, G.-E., Mo, X., Zumar, C., Stoica,
I., Gonzalez, J., and Tumanov, A. (2020). Inferline:
latency-aware provisioning and scaling for prediction
serving pipelines. In Proceedings of the 11th ACM
Symposium on Cloud Computing, pages 477–491.
Goswami(Mukherjee), B., Sarkar, J., Saha, S., Kar, S.,
and Sarkar, P. (2019). Alvec: Auto-scaling by lotka
volterra elastic cloud: A qos aware non linear dynam-
ical allocation model. Simulation Modelling Practice
and Theory, 93:262–292. Modeling and Simulation of
Cloud Computing and Big Data.
Imdoukh, M., Ahmad, I., and Alfailakawi, M.
(2020). Machine learning based auto-
scaling for containerized applications. Neu-
ral Computing and Applications, pages
http://link.springer.com/article/10.1007/s00521–
019.
Lemos, R., Giese, H., M
¨
uller, H., Andersson, J., Litoiu,
M., Schmerl, B., Tamura, G., Villegas, N., Vogel, T.,
Weyns, D., Baresi, L., Becker, B., Bencomo, N., Brun,
Y., Cukic, B., Desmarais, R., Dustdar, S., Engels,
G., and Wuttke, J. (2013). Software Engineering for
Self-Adaptive Systems: A Second Research Roadmap,
pages 1–32.
Marathe, N., Gandhi, A., and Shah, J. M. (2019). Docker
swarm and kubernetes in cloud computing environ-
ment. 2019 3rd International Conference on Trends in
Electronics and Informatics (ICOEI), pages 179–184.
Merkouche, S. and Bouanaka, C. (2022a). A proactive
formal approach for microservice-based applications
auto-scaling. In proceedings of CEUR Workshop, vol-
ume 3176, pages 15–28.
Merkouche, S. and Bouanaka, C. (2022b). Tera-scheduler
for a dependency-based orchestration of microser-
vices. In 2022 International Conference on Advanced
Aspects of Software Engineering (ICAASE), pages 1–
8.
Nguyen, T.-T., Yeom, Y.-J., Kim, T., Park, D.-H., and Kim,
S. (2020). Horizontal pod autoscaling in kubernetes
for elastic container orchestration. Sensors, 20(16).
Niemel
¨
a, P. and Hyyr
¨
o, H. (2019). Migrating learning
management systems towards microservice architec-
ture. In SSSME-2019: Joint Proceedings of the Inforte
Summer School on Software Maintenance and Evolu-
tion (pp. 10- 20). (CEUR Workshop Proceedings; Vol.
2520). CEUR-WS.
Palumbo, A. (2022). Literature review on kubernetes & red-
hat openshift container platform.
Rossi, F., Cardellini, V., and Presti, F. L. (2020). Hierar-
chical scaling of microservices in kubernetes. In 2020
IEEE International Conference on Autonomic Com-
puting and Self-Organizing Systems (ACSOS), pages
28–37.
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