Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
Scalability in Microservices: A systematic literature review
DOI:
https://doi.org/10.24215/16666038.25.e11Keywords:
Microservices Architecture, Scalability, Scalability Challenges, AutoscalingAbstract
The scalability of microservices architectures is crucial for modern software systems, yet it presents significant challenges due to their inherent complexities. This study aims to systematically review existing literature on the scalability of microservices, identifying key strategies, challenges, and emerging trends. We conducted a systematic literature review following the PRISMA guidelines, analyzing 44 scholarly articles that specifically address the scalability of microservices. The review focused on various scaling approaches, metrics, and the effectiveness of autoscaling mechanisms. Our findings reveal a diverse body of literature with a predominant focus on autoscaling strategies, particularly those utilizing machine learning. Key challenges identified include accurate metrics collection, dynamic scaling decision-making, and balancing performance with cost and security. While progress has been made in addressing scalability challenges, significant gaps remain, particularly in standardizing autoscaling metrics. Future research should focus on developing robust, adaptive autoscaling systems that can effectively manage real-world complexities and dynamic workloads, ensuring both performance and cost optimization in microservices architectures.
Downloads
Download data is not yet available.
References
V. L. Nogueira et al., “Insights on microservice architecture through the eyes of industry practitioners,” arXiv preprint arXiv:2408.10434, 2024. doi: 10.48550/arXiv.2408.10434.
G. Blinowski, A. Ojdowska, y A. Przybyłek, “Monolithic vs. microservice architecture: A performance and scalability evaluation,” IEEE Access, vol. 10, pp. 20 357–20 374, 2022. doi: 10.1109/ACCESS.2022.3152803.
N. Bjørndal et al., “Migration from monolith to microservices: Benchmarking a case study,” Tech. Rep., 2020. doi: 10.13140/RG.2.2.27715.14883.
R. Capuano y H. Muccini, “A systematic literature review on migration to microservices: a quality attributes perspective,” en 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C). IEEE, 2022, pp. 120–123. doi: 10.1109/ICSA-C54293.2022.00030.
S. Hussein, M. Lahami, y M. Torjmen, “Assessing the quality of microservice and monolithic-based architectures: A systematic literature review,” Operational Research in Engineering Sciences: Theory and Applications, vol. 7, no. 2, 2024. doi: 0.31181/oresta/070220.
A. Hilali, H. Hafiddi, y Z. El Akkaoui, “Microservices adaptation using machine learning: A systematic mapping study,” ICSOFT, pp. 521–532, 2021.
S. Hassan, R. Bahsoon, y R. Kazman, “Microservice transition and its granularity problem: A systematic mapping study,” Software: Practice and Experience, vol. 50, no. 9, pp. 1651–1681, 2020. doi: 10.1002/spe.2869.
M. S. Hamzehloui, S. Sahibuddin, y K. Salah, “A systematic mapping study on microservices,” en Recent Trends in Data Science and Soft Computing: Proceedings of the 3rd International Conference of Reliable Information and Communication Technology (IRICT 2018). Springer, 2019, pp. 1079–1090. doi: 10.1007/978-3-319-99007-1_100.
Z. Stojanov et al., “Research trends and recommendations for future microservices research,” vol. 36, no. 1, pp. 105–130, 2024. doi: 10.15514/ISPRAS-2024-36(1)-7.
S. Alharthi et al., “Auto-scaling techniques in cloud computing: Issues and research directions,” Sensors, vol. 24, no. 17, p. 5551, 2024. doi: 10.3390/s24175551.
I. Ghani et al., “Microservice testing approaches: A systematic literature review,” International Journal of Integrated Engineering, vol. 11, no. 8, pp. 65–80, 2019. doi: 10.30880/ijie.2019.11.08.008.
I. K. Aksakalli et al., “Deployment and communication patterns in microservice architectures: A systematic literature review,” Journal of Systems and Software, vol. 180, p. 111014, 2021. doi: 10.1016/j.jss.2021.111014.
P. Haindl, P. Kochberger, y M. Sveggen, “A systematic literature review of inter-service security threats and mitigation strategies in microservice architectures,” IEEE Access, 2024. doi: 10.1109/ACCESS.2024.3406500.
D. Taibi, V. Lenarduzzi, y C. Pahl, “Architectural patterns for microservices: a systematic mapping study,” en CLOSER 2018: Proceedings of the 8th International Conference on Cloud Computing and Services Science; Funchal, Madeira, Portugal, 19-21 March 2018. SciTePress, 2018. Disponible en: https://doi.org/19/03/201821/03/2018.
D. Taibi, V. Lenarduzzi, y C. Pahl, “Continuous architecting with microservices and devops: A systematic mapping study,” en Cloud Computing and Services Science: 8th International Conference, CLOSER 2018, Funchal, Madeira, Portugal, March 19-21, 2018, Revised Selected Papers 8. Springer, 2019, pp. 126–151. doi: 10.1007/978-3-030-29193-8_7.
M. Söylemez, B. Tekinerdogan, y A. Kolukısa Tarhan, “Challenges and solution directions of microservice architectures: A systematic literature review,” Applied sciences, vol. 12, no. 11, p. 5507, 2022. doi: 10.3390/app12115507.
H. Vural, M. Koyuncu, y S. Guney, “A systematic literature review on microservices,” en Computational Science and Its Applications–ICCSA 2017: 17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part VI 17. Springer, 2017, pp. 203–217. doi: 10.1007/978-3-319-62407-5_14.
S. Li et al., “Understanding and addressing quality attributes of microservices architecture: A systematic literature review,” Information and software technology, vol. 131, p. 106449, 2021. doi: 10.1016/j.infsof.2020.106449.
N. Alshuqayran, N. Ali, y R. Evans, “A systematic mapping study in microservice architecture,” en 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA), 2016, pp. 44–51. doi: 10.1109/SOCA.2016.15.
V. Bushong et al., “On microservice analysis and architecture evolution: A systematic mapping study,” Applied Sciences, vol. 11, no. 17, p. 7856, 2021. doi: 10.3390/app11177856.
S. Lungu y M. Nyirenda, “Current trends in the management of distributed transactions in micro-services architectures: A systematic literature review,” Open Journal of Applied Sciences, vol. 14, no. 9, pp. 2519–2543, 2024. doi: 10.4236/ojapps.2024.149167.
R. Sarkis-Onofre et al., “How to properly use the prisma statement,” Systematic Reviews, vol. 10, pp. 1–3, 2021. doi: 10.1186/s13643-021-01671-z.
A.-W. Harzing, “Publish or Perish — harzing.com,” 2016. [Online]. Disponible en: https://harzing.com/resources/publish-or-perish. [Accedido: 06-Abr.-2024].
T. Heyman, D. Preuveneers, y W. Joosen, “Scalar: Systematic scalability analysis with the universal scalability law,” en 2014 International Conference on Future Internet of Things and Cloud. IEEE, 2014, pp. 497–504. doi: 10.1109/FiCloud.2014.88.
D. Monteiro et al., “Beethoven: an event-driven lightweight platform for microservice orchestration,” en Software Architecture: 12th European Conference on Software Architecture, ECSA 2018, Madrid, Spain, September 24–28, 2018, Proceedings 12. Springer, 2018, pp. 191–199. doi: 10.1007/978-3-030-00761-4_13.
L. Baresi y G. Quattrocchi, “Cocos: A scalable architecture for containerized heterogeneous systems,” en 2020 IEEE International Conference on Software Architecture (ICSA). IEEE, 2020, pp. 103–113. doi: 10.1109/ICSA47634.2020.00018.
L. L. Jiménez y O. Schelén, “Docma: A decentralized orchestrator for containerized microservice applications,” en 2019 IEEE Cloud Summit. IEEE, 2019, pp. 45–51. doi: 10.1109/CloudSummit47114.2019.00014.
G. Márquez, M. M. Villegas, y H. Astudillo, “A pattern language for scalable microservices-based systems,” en Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, 2018, pp. 1–7. doi: 10.1145/3241403.3241429.
D. Gesvindr, J. Davidek, y B. Buhnova, “Design of scalable and resilient applications using microservice architecture in paas cloud,” en ICSOFT, 2019, pp. 619–630. doi: 10.5220/0007842906190630.
Y. Zhang, Y. Gan, y C. Delimitrou, “µqsim: Enabling accurate and scalable simulation for interactive microservices,” en 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE, 2019, pp. 212–222. doi: 10.1109/ISPASS.2019.00034.
S. Henning y W. Hasselbring, “A configurable method for benchmarking scalability of cloud-native applications,” Empirical Software Engineering, vol. 27, no. 6, p. 143, 2022. doi: 10.1007/s10664-022-10162-1.
W. Hasselbring y G. Steinacker, “Microservice architectures for scalability, agility and reliability in e-commerce,” en 2017 IEEE International Conference on Software Architecture Workshops (ICSAW). IEEE, 2017, pp. 243–246. doi: 10.1109/ICSAW.2017.11.
N. Dragoni et al., “Microservices: How to make your application scale,” en Perspectives of System Informatics: 11th International Andrei P. Ershov Informatics Conference, PSI 2017, Moscow, Russia, June 27-29, 2017, Revised Selected Papers 11. Springer, 2018, pp. 95–104. doi: 10.1007/978-3-319-74313-4_8.
Z. Wang, Y. Xia, C. Sun, y L. Cheng, “Research on microservice application performance monitoring framework and elastic scaling mode,” en Journal of Physics: Conference Series, vol. 1617, no. 1. IOP Publishing, 2020, p. 012048. doi: 10.1088/1742-6596/1617/1/012048.
K.-H. Chow et al., “Scad: Scalability advisor for interactive microservices on hybrid clouds,” en Companion of the 2023 International Conference on Management of Data, 2023, pp. 127–130. doi: 10.1145/3555041.3589718.
N. Agnihotri y A. K. Sharma, “Evaluating paas scalability and improving performance using scalability improvement systems,” IJRET: International Journal of Research in Engineering and Technology eISSN, pp. 2319–1163, 2014.
M. Rusek, G. Dwornicki, y A. Orłowski, “A decentralized system for load balancing of containerized microservices in the cloud,” en Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016) 19. Springer, 2017, pp. 142–152. doi: 10.1007/978-3-319-48944-5_14.
Y. Niu, F. Liu, y Z. Li, “Load balancing across microservices,” en IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 2018, pp. 198–206. doi: 10.1109/INFOCOM.2018.8486300.
Y. Liang y Y. Lan, “Tclbm: A task chain-based load balancing algorithm for microservices,” Tsinghua Science and Technology, vol. 26, no. 3, pp. 251–258, 2020. doi: 10.26599/TST.2019.9010032.
D. Müssig et al., “Highly scalable microservice-based enterprise architecture for smart ecosystems in hybrid cloud environments,” en International Conference on Enterprise Information Systems, vol. 2. SCITEPRESS, 2017, pp. 454–459. doi: 10.5220/0006373304540459.
C.-C. Crecana y F. Pop, “Monitoring-based auto-scalability across hybrid clouds,” en Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1087–1094. doi: 10.1145/3167132.3167248.
G. Yu, P. Chen, y Z. Zheng, “Microscaler: Automatic scaling for microservices with an online learning approach,” en 2019 IEEE International Conference on Web Services (ICWS). IEEE, 2019, pp. 68–75. doi: 10.1109/ICWS.2019.00023.
G. Yu, P. Chen, y Z. Zheng, “Microscaler: Cost-effective scaling for microservice applications in the cloud with an online learning approach,” IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 1100–1116, 2020. doi: 10.1109/TCC.2020.2985352.
M. Gotin et al., “Investigating performance metrics for scaling microservices in cloudiot-environments,” en Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering, 2018, pp. 157–167. doi: 10.1145/3184407.3184430.
S. Merkouche y C. Bouanaka, “Tera-scaler for a proactive auto-scaling of e-business microservices,” pp. 448–455, 2023. doi: 10.5220/0012093500003538.
H. Ahmad et al., “Smart hpa: A resource-efficient horizontal pod auto-scaler for microservice architectures,” en 2024 IEEE 21st International Conference on Software Architecture (ICSA). IEEE, 2024, pp. 46–57. doi: 10.1109/ICSA59870.2024.00013.
L. Bacchiani et al., “Proactive–reactive microservice architecture global scaling,” Journal of Systems and Software, vol. 220, p. 112262, 2025. doi: 10.1016/j.jss.2024.112262.
A. A. Khaleq y I. Ra, “Agnostic approach for microservices autoscaling in cloud applications,” en 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2019, pp. 1411–1415. doi: 10.1109/CSCI49370.2019.00264.
Y. Gan et al., “Leveraging deep learning to improve the performance predictability of cloud microservices,” arXiv preprint arXiv:1905.00968, 2019. doi: 10.48550/arXiv.1905.00968.
F. Klinaku, M. Frank, y S. Becker, “Caus: An elasticity controller for a containerized microservice,” en Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, 2018, pp. 93–98. doi: 10.1145/3185768.3186296.
M. Abdullah et al., “Predictive autoscaling of microservices hosted in fog microdata center,” IEEE Systems Journal, vol. 15, no. 1, pp. 1275–1286, 2020. doi: 10.1109/JSYST.2020.2997518.
L. M. Al Qassem et al., “Proactive random-forest autoscaler for microservice resource allocation,” IEEE Access, vol. 11, pp. 2570–2585, 2023. doi: 10.1109/ACCESS.2023.3234021.
I. Prachitmutita et al., “Auto-scaling microservices on iaas under sla with cost-effective framework,” en 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2018, pp. 583–588. doi: 10.1109/ICACI.2018.8377525.
M. Abdullah et al., “Burst-aware predictive autoscaling for containerized microservices,” IEEE Transactions on Services Computing, vol. 15, no. 3, pp. 1448–1460, 2020. doi: 10.1109/TSC.2020.2995937.
H. Ahmad et al., “Towards resource-efficient reactive and proactive auto-scaling for microservice architectures,” Journal of Systems and Software, p. 112390, 2025. doi: 10.1016/j.jss.2025.112390.
A. A. Khaleq y I. Ra, “Intelligent autoscaling of microservices in the cloud for real-time applications,” IEEE Access, vol. 9, pp. 35 464–35 476, 2021. doi: 10.1109/ACCESS.2021.3061890.
M. B. Taha et al., “Proactive auto-scaling for service function chains in cloud computing based on deep learning,” IEEE Access, 2024. doi: 10.1109/ACCESS.2024.3375772.
N. H. Do et al., “A scalable routing mechanism for stateful microservices,” en 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN). IEEE, 2017, pp. 72–78. doi: 10.1109/ICIN.2017.7899252.
H. Khazaei et al., “Elascale: Autoscaling and monitoring as a service,” arXiv preprint arXiv:1711.03204, 2017. doi: 10.48550/arXiv.1711.03204.
E. Casalicchio y V. Perciballi, “Auto-scaling of containers: The impact of relative and absolute metrics,” en 2017 IEEE 2nd International Workshops on Foundations and Applications of Self Systems (FAS* W)*. IEEE, 2017, pp. 207–214. doi: 10.1109/FAS-W.2017.149.
P. Agarwal y J. Lakshmi, “Cost aware resource sizing and scaling of microservices,” en Proceedings of the 2019 4th International Conference on Cloud Computing and Internet of Things, 2019, pp. 66–74. doi: 10.1145/3361821.3361823.
T. Lin y A. Leon-Garcia, “Towards a client-centric qos auto-scaling system,” en NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020, pp. 1–9. doi: 10.1109/NOMS47738.2020.9110450.
A. Bhole, “Enhancing performance and scalability in microservices,” Journal of Engineering and Applied Sciences Technology, pp. 1–9, Sep. 2024. doi: 10.47363/JEAST/2024(6)E162.
K. Hu et al., “Msars: A meta-learning and reinforcement learning framework for slo resource allocation and adaptive scaling for microservices,” arXiv preprint arXiv:2409.14953, 2024. doi: 10.48550/arXiv.2409.14953.
A. Cholomskis, O. Pozdniakova, y D. Mažeika, “Cloud software performance metrics collection and aggregation for auto-scaling module,” en Information and Software Technologies: 24th International Conference, ICIST 2018, Vilnius, Lithuania, October 4–6, 2018, Proceedings 24. Springer, 2018, pp. 130–138. doi: 10.1007/978-3-319-99972-2_10.
A. Jindal, V. Podolskiy, y M. Gerndt, “Performance modeling for cloud microservice applications,” en Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering, 2019, pp. 25–32. doi: 10.1145/3297663.3310309.
E. Casalicchio, “A study on performance measures for auto-scaling cpu-intensive containerized applications,” Cluster Computing, vol. 22, no. 3, pp. 995–1006, 2019. doi: 10.1007/s10586-018-02890-1.
A. Kwan et al., “Hyscale: Hybrid and network scaling of dockerized microservices in cloud data centres,” en 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2019, pp. 80–90. doi: 10.1109/ICDCS.2019.00017.
V. Podolskiy, A. Jindal, y M. Gerndt, “Multilayered autoscaling performance evaluation: can virtual machines and containers co–scale?” International journal of applied mathematics and computer science, vol. 29, no. 2, pp. 227–244, 2019. doi: 10.2478/amcs-2019-0017.
Y. M. Ramirez, V. Podolskiy, y M. Gerndt, “Capacity-driven scaling schedules derivation for coordinated elasticity of containers and virtual machines,” en 2019 IEEE International conference on autonomic computing (ICAC). IEEE, 2019, pp. 177–186. doi: 10.1109/ICAC.2019.00029.
N. C. Coulson, S. Sotiriadis, y N. Bessis, “Adaptive microservice scaling for elastic applications,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4195–4202, 2020. doi: 10.1109/JIOT.2020.2964405.
C. K. Rudrabhatla, “A quantitative approach for estimating the scaling thresholds and step policies in a distributed microservice architecture,” IEEE Access, vol. 8, pp. 180 246–180 254, 2020. doi: 10.1109/ACCESS.2020.3028310.
B. Choi et al., “phpa: A proactive autoscaling framework for microservice chain,” en Proceedings of the 5th Asia-Pacific Workshop on Networking, 2021, pp. 65–71. doi: 10.1145/3469393.3469401.
H. X. Nguyen, S. Zhu, y M. Liu, “Graph-phpa: graph-based proactive horizontal pod autoscaling for microservices using lstm-gnn,” en 2022 IEEE 11th International Conference on Cloud Networking (CloudNet). IEEE, 2022, pp. 237–241. doi: 10.1109/CloudNet55617.2022.9978781.
Y. Kim et al., “Improved q network auto-scaling in microservice architecture,” Applied Sciences, vol. 12, no. 3, p. 1206, 2022. doi: 10.3390/app12031206.
M. ZargarAzad y M. Ashtiani, “An auto-scaling approach for microservices in cloud computing environments,” Journal of Grid Computing, vol. 21, no. 4, p. 73, 2023. doi: 10.1007/s10723-023-09713-7.
V. M. Mostofi et al., “Trace-driven scaling of microservice applications,” IEEE Access, vol. 11, pp. 29 360–29 379, 2023. doi: 10.1109/ACCESS.2023.3260069.
J. Santos et al., “gym-hpa: Efficient auto-scaling via reinforcement learning for complex microservice-based applications in kubernetes,” en NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2023, pp. 1–9. doi: 10.1109/NOMS56928.2023.10154298.
Downloads
Published
2025-10-22
Issue
Section
Original Articles
License
Copyright (c) 2025 Mr. Nishal Gurung, Dr. Sushil Shrestha, Dr. Rajani Chulyadyo
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
[1]
“Scalability in Microservices: A systematic literature review”, JCS&T, vol. 25, no. 2, p. e11, Oct. 2025, doi: 10.24215/16666038.25.e11.
