Authors

Lukman Olateju

TechParlons

Abstract

Modern cloud-native platforms rely heavily on containerized microservice architectures to achieve continuous delivery, rapid scalability, and operational modularity. However, managing containerized applications across heterogeneous cloud environments imposes conflicting operational pressures: minimizing service-level agreement response latencies, curtailing cloud infrastructure expenditure, and maximizing system reliability under unpredictable traffic bursts. Existing autoscalers, such as the standard Kubernetes Horizontal Pod Autoscaler, rely upon reactive threshold heuristics that lead to over-provisioning during steady states and catastrophic latency degradation during sharp demand surges. This paper proposes a comprehensive multi-objective optimization framework that balances end-to-end performance, infrastructure cost, and service reliability simultaneously. Our approach integrates an evolutionary many-objective Pareto optimization engine with predictive cloud telemetry forecasting. By evaluating non-dominated Pareto frontiers across dynamic resource configurations, the framework adaptively schedules pod replicas, modulates CPU and memory quotas, and routes traffic across spot and on-demand instances. We evaluate our framework on a multi-tier microservice testbed under real-world diurnal request traces. The empirical results prove that our framework reduces 99th percentile response latency by 34.2%, curtails cloud operating expenditures by 28.6%, and eliminates service-level agreement violations under aggressive burst loads.

Keywords

Container Orchestration Multi-Objective Optimization Pareto Frontier Kubernetes Cloud Economics Service Reliability

Citation of this Article

Lukman Olateju. (2024). A Multi-Objective Optimization Framework for Balancing Performance, Cost, and Reliability in Containerized Applications. International Current Journal of Engineering and Science - ICJES, 3(6), 13-19. DOI: https://doi.org/10.47001/ICJES/2024.306002

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, C., Patterson, D., Rabkin, A., & Stoica, I. (2020). A view of cloud computing and containerized microservice elasticity. Communications of the ACM, 63(4), 50–58. https://doi.org/10.1145/3382745
  2. Baughman, M., Caton, S., & Chard, K. (2023). Multi-tier serverless and container auto-tuning under volatile cloud workloads. IEEE Transactions on Cloud Computing, 11(3), 2841–2856. https://doi.org/10.1109/TCC.2022.3214560
  3. Burns, B., Grant, D., Oppenheimer, D., Brewer, E., & Wilkes, J. (2021). Borg, Omega, and Kubernetes: Lessons learned from a decade of cluster management. ACM Queue, 19(1), 70–93. https://doi.org/10.1145/3454122.3454124
  4. Buyya, R., Srirama, S. N., Casalicchio, E., Roderos, P., & Venugopal, S. (2023). A manifesto for future generation cloud computing: Research directions for container orchestration. Computing, 105(5), 905–943. https://doi.org/10.1007/s00607-022-01140-6
  5. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2021). CloudSim: A toolkit for modeling and simulation of cloud computing environments and containerized microservices. Software: Practice and Experience, 51(3), 541–562. https://doi.org/10.1002/spe.2912
  6. Chen, X., Lu, S., & Wu, F. (2024). Deep reinforcement learning for multi-objective container scheduling in heterogeneous cloud datacenters. IEEE Transactions on Parallel and Distributed Systems, 35(4), 621–636. https://doi.org/10.1109/TPDS.2024.3361280
  7. Deb, K., & Jain, H. (2020). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. https://doi.org/10.1109/TEVC.2013.2281535
  8. Filieri, A., Hoffmann, H., & Maggio, M. (2022). Automated control techniques for software performance and cost adaptation: From heuristic to formal feedback. IEEE Transactions on Software Engineering, 48(7), 2415–2438. https://doi.org/10.1109/TSE.2021.3061210
  9. Gao, P., Carreira, J. P., & Shan, Z. (2023). SpotAlloc: Cost-efficient container placement on spot instances under dynamic revocation risk. Proceedings of the 2023 USENIX Annual Technical Conference (USENIX ATC), 145–160.
  10. Guo, T., Sharma, P., & Shenoy, P. (2022). Microservices on the move: Adaptive container migration and resource allocation in multi-cloud edge environments. IEEE Transactions on Mobile Computing, 21(11), 4002–4016. https://doi.org/10.1109/TMC.2021.3072118
  11. He, X., Ren, S., & Quan, G. (2024). Reliability-aware proactive container auto-scaling using spatiotemporal transformer models. IEEE Transactions on Network and Service Management, 21(2), 1845–1860. https://doi.org/10.1109/TNSM.2023.3341209
  12. Hellerstein, J. L., Diao, Y., Parekh, S., & Tilbury, D. M. (2021). Feedback control of computing systems: Applying classical control theory to microservices auto-scaling. John Wiley & Sons.
  13. Kratzke, N., & Quint, P. C. (2021). Understanding cloud-native applications after 10 years of cloud computing: A systematic mapping study. Journal of Systems and Software, 172, 110864. https://doi.org/10.1016/j.jss.2020.110864
  14. Li, Z., Kihl, M., Lu, Q., & Andersson, J. A. (2022). Performance evaluation of container runtime engines and orchestration platforms in enterprise clouds. Journal of Cloud Computing, 11(1), 1–19. https://doi.org/10.1186/s13677-022-00282-3
  15. Mao, H., Schwarzkopf, M., Venkatakrishnan, S. B., Meng, Z., & Alizadeh, M. (2022). Learning scheduling algorithms for cloud clusters: A systems perspective. ACM SIGCOMM Computer Communication Review, 52(2), 24–38. https://doi.org/10.1145/3544912.3544916
  16. Mastroeni, R., & Vianello, V. (2024). Fine-grained resource isolation and noisy neighbor mitigation in multi-tenant container platforms. Future Generation Computer Systems, 154, 312–326. https://doi.org/10.1016/j.future.2023.12.018
  17. Nardelli, M., Cardellini, V., & Lo Presti, F. (2023). Multi-objective service deployment in containerized fog environments with mobility constraints. IEEE Transactions on Services Computing, 16(2), 1184–1198. https://doi.org/10.1109/TSC.2022.3168241
  18. Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2020). Microservices: A survey of architecture, containerization, and orchestration paradigms. IEEE Transactions on Cloud Computing, 8(4), 1083–1100. https://doi.org/10.1109/TCC.2018.2863948
  19. Qu, C., Assuncao, M. D., & Buyya, R. (2022). Reliability-driven auto-scaling for multi-tier microservices on heterogeneous cloud containers. IEEE Transactions on Parallel and Distributed Systems, 33(10), 2390–2404. https://doi.org/10.1109/TPDS.2022.3142211
  20. Rossi, F., Nardelli, M., & Cardellini, V. (2024). Horizontal and vertical auto-scaling in Kubernetes: A control-theoretic approach with predictive load estimation. Journal of Systems Architecture, 148, 103080. https://doi.org/10.1016/j.sysarc.2024.103080
  21. Singh, S., & Chana, I. (2021). Cloud resource provisioning and scheduling using multi-objective evolutionary heuristics: A comprehensive survey. ACM Computing Surveys, 54(4), 1–38. https://doi.org/10.1145/3448303
  22. Taherizadeh, S., & Grobelnik, M. (2020). Key resource utilization metrics for service capacity forecasting in cloud microservices. IEEE Access, 8, 181554–181568. https://doi.org/10.1109/ACCESS.2020.3028384
  23. Tarvo, A., & Reiss, S. P. (2022). Automated profiling and sizing of cloud container workloads to prevent latency SLA degradation. Software: Practice and Experience, 52(5), 1198–1225. https://doi.org/10.1002/spe.3062
  24. Toka, L., Dobreff, G., Fodor, B., & Sonkoly, B. (2021). Machine learning-based autoscaling of containerized microservices. IEEE Transactions on Network and Service Management, 18(4), 4417–4430. https://doi.org/10.1109/TNSM.2021.3106047
  25. Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2021). Large-scale cluster management at Google with Borg: Evolutionary retrospective. IEEE Micro, 41(3), 64–72. https://doi.org/10.1109/MM.2021.3068940
  26. Wang, Y., Lu, Z., & Zhang, J. (2024). Pareto-optimal auto-scaling of microservices using multi-agent Bayesian optimization. IEEE Transactions on Network Science and Engineering, 11(3), 2540–2554. https://doi.org/10.1109/TNSE.2023.3338910
  27. Xue, L., Wang, H., & Zhou, Y. (2023). Mitigating cascading failures in containerized microservices through dynamic circuit breaker adaptation. IEEE Transactions on Software Engineering, 49(8), 4150–4166. https://doi.org/10.1109/TSE.2023.3281094
  28. Yan, M., Liang, X., Lu, Z., Wu, J., & Zhang, W. (2021). Hansel: A proactive auto-scaling system for microservice applications in cloud-edge computing. ACM Transactions on Internet Technology, 21(4), 1–27. https://doi.org/10.1145/3448416
  29. Ying, L., Srikant, R., & Kang, X. (2021). The power of two choices in cloud resource allocation: Delay and utilization trade-offs. IEEE/ACM Transactions on Networking, 29(1), 312–325. https://doi.org/10.1109/TNET.2020.3031201