Intelligent Consensus Optimization Approach Toward Coherent Resource Management in Microservice Systems

Document Type : Original Article

Authors

1 Assistant Professor, Department of Computer Engineering, Islamic Azad University, Zanjan, Iran

2 Department of Computer Engineering, Islamic Azad University, Zanjan Branch, Zanjan

Abstract

Microservice architectures, as the dominant design paradigm for cloud systems, offer several advantages such as flexibility and scalability. However, they face challenges such as ensuring data integrity, dealing with node failures, and optimal resource allocation. In this research, a new approach and intelligent optimization mechanism are proposed that, by utilizing the Paxos consensus protocol, leads to improved performance, resilience, and stability in microservice systems. The proposed approach, emphasizing intelligent coordination in changing service states, reducing latency, and dynamically allocating resources, is an effective alternative to traditional solutions based on statistical models and evolutionary algorithms. Evaluations in dynamic cloud environments with variable workloads present that the proposed approach is capable of providing significant improvements in fundamental metrics such as response time (25%), throughput (30%), efficiency (20%), consistency (99.9%), and fault tolerance (99.8%). Also, comparison with existing frameworks present that combining consensus algorithms with intelligent optimization mechanisms and effective resource management enables achieving stable performance in unstable and variable cloud conditions. These results indicate the high capacity of the proposed approach for utilizing in modern and complex microservice architectures.

Keywords

Main Subjects

References

[1]     Peidro, J. E., Muñoz-Escoí, F. D., & Bernabéu-Aubán, J. M. (2024). Modeling microservice architectures. J. Syst. Softw., 213, 112041. https://doi.org/10.1016/j.jss.2024.112041.
[2]     Lelovic, L., Huzinga, A., Goulis, G., Kaur, A., Boone, R., Muzrapov, U., ... & Cerny, T. (2024). Change impact analysis in microservice systems: A systematic literature review. Journal of Systems and Software, 112241.
[3]     Howard, H., Malkhi, D., & Spiegelman, A. (2016). Flexible paxos: Quorum intersection revisited. arXiv preprint arXiv:1608.06696.
[4]     Ahmed, A. (2018). Programming Languages and Systems: 27th European Symposium on Programming, ESOP 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings. Springer Nature.
[5]     Baboi, M., Iftene, A., & Gîfu, D. (2019). Dynamic microservices to create scalable and fault tolerance architecture. Procedia Computer Science, 159, 1035-1044.
[6]     Henning, S., & Hasselbring, W. (2024). Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud. Journal of Systems and Software, 208, 111879.
[7]     Lin, W., Sheng, X., & Qi, L. (2019). An Optimized Multi-Paxos Consensus Protocol for Practical Cloud Storage Applications. In Cyberspace Safety and Security: 11th International Symposium, CSS 2019, Guangzhou, China, December 1–3, 2019, Proceedings, Part I 11 (pp. 575-584). Springer International Publishing.
[8]     Ailijiang, A., Charapko, A., & Demirbas, M. (2016, August). Consensus in the cloud: Paxos systems demystified. In 2016 25th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-10). IEEE.
[9]     Nakarmi, A., Kesharwani, H., Mallick, T., Jhingran, S., & Raj, G. (2024, April). A Comprehensive Study on Optimization Techniques for Microservices Deployment. In 2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT) (pp. 133-140). IEEE.
[10] Dong, H., & Liu, S. (2024). Asynchronous consensus quorum read: Pioneering read optimization for asynchronous consensus protocols. Electronics, 13(3), 481.
[11] Lampport, L. (1998). The part-time parliament. ACM Transactions on Computer Systems, 16(2), 133-169.
[12] Ongaro, D., & Ousterhout, J. (2014). In search of an understandable consensus algorithm. In 2014 USENIX annual technical conference (USENIX ATC 14) (pp. 305-319).
[13] Junqueira, F., Reed, B., and Serafini, M., “ZooKeeper’s atomic broadcast and linearizable operations,” in Proceedings of the Large Scale Distributed Systems and Middleware Workshop (LADIS’11), 2011.
[14] Kamil, S. N. S., Thomas, N., & Elsanosi, I. (2021, October). Performance evaluation of zookeeper atomic broadcast protocol. In EAI International Conference on Performance Evaluation Methodologies and Tools (pp. 56-71).
[15] Castro, M., & Liskov, B. (1999, February). Practical byzantine fault tolerance. In OsDI (Vol. 99, No. 1999, pp. 173-186).
[16] da Silva Pinheiro, T. F., Pereira, P., Silva, B., & Maciel, P. (2023). A performance modeling framework for microservices-based cloud infrastructures. The Journal of Supercomputing, 79(7), 7762-7803.
[17] Nawab, F., & Sadoghi, M. (2024). Consensus in Data Management: With Use Cases in Edge-Cloud and Blockchain Systems. Proceedings of the VLDB Endowment, 17(12), 4233-4236.
[18] Mwotil, A., Anderson, T., Kanagwa, B., Stavrinos, T., & Bainomugisha, E. (2024). LowPaxos: State Machine Replication for Low Resource Settings. IEEE Acces.
[19] Turkkan, B., Rodrigues, E., Kosar, T., Charapko, A., Ailijiang, A., & Demirbas, M. (2024). How to Evaluate Distributed Coordination Systems?--A Survey and Analysis. arXiv preprint arXiv:2403.09445.
[20] Johnson, R. (2025). Practical Replication Architectures and Protocols: Definitive Reference for Developers and Engineers. HiTeX Press.
[21] Amini, E., Miši, J., & Miši, V. B. (2025). Paxos With Priorities for Blockchain Applications. IEEE Transactions on Network and Service Management.
[22] Ferzo, B., & Zeebaree, S. R. (2024). Distributed Transactions in Cloud Computing: A Review Reliability and Consistency. The Indonesian Journal of Computer Science, 13(3).
[23] Jiang, T., Huang, X., Song, S., Wang, C., & Wang, J. (2024, May). On Tuning Raft for IoT Workload in Apache IoTDB. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 5307-5319). IEEE.
[24] Fareghzadeh, N., Seyyedi, M. A., & Mohsenzadeh, M. (2018). Dynamic performance isolation management for cloud computing services. The Journal of Supercomputing, 74, 417-455.
[25] Jiang, T., Huang, X., Song, S., Wang, C., & Wang, J. (2024, May). On Tuning Raft for IoT Workload in Apache IoTDB. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 5307-5319). IEEE.
[26] Fareghzadeh, N. (2022). An architecture supervisor scheme toward performance differentiation and optimization in cloud systems. The Journal of Supercomputing, 78(1), 1532-1563.
[27] Charapko, A., Ailijiang, A., & Demirbas, M. (2021, June). Pigpaxos: Devouring the communication bottlenecks in distributed consensus. In Proceedings of the 2021 International Conference on Management of Data (pp. 235-247).
[28] Hao, W., Xu, G., Wang, J., & Wen, Y. (2024, October). PPaxos: An Adaptive Pull-Based Group Consensus Protocol for Edge Networks. In 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS) (pp. 761-768). IEEE.
[29] Montgomery, D. C. (2001). Design and analysis of experiments, John Wiley & Sons. Inc., New York, 1997, 200-1.
[30] Fareghzadeh, N. (2019). Service Contract-Aware Quality Supervisory Methodology in Cloud Systems. Journal of Quality Engineering and Management, 9(2), 172-185.
[31] Xie, Y., et al. (2017). Characterizing delay variability in cloud data center networks. Proceedings of ACM SIGCOMM, 494–507.
[32] [32] M. H. Mohabbati Hamidi and M. Abbasi, "Efficient mechanism for determining a function for workload distribution in fog computing using classifier systems," The Journal of Theoretical and Applied Machine Intelligence, vol. 2, no. 1, pp. 1-13, 2024, doi: 10.22034/abmir.2024.20271.1030.
[33] [33] M. M. Hosseini and H. Zargari, "Energy-efficient clustering in multi-hop wireless sensor networks using multi-objective particle swarm optimization," The Journal of Theoretical and Applied Machine Intelligence, vol. 2, no. 2, pp. 67-81, 2025, doi: 10.22034/abmir.2025.22425.1077.
[34] ​[34] A. Doldi, A. Mozidi, and M. B. Gorji, "Designing a cloud computing based human resource optimization model for Tejarat Bank," Information Management Sciences and Technologies, vol. 10, no. 2, pp. 235-259, 2024, doi: 10.22091/stim.2023.9683.1980
Applied and basic Machine intelligence research
Volume 3, Issue 1
August 2025
Pages 145-161

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