Operational Expense Optimization for Remote Computing Repositories within Farm Credit Management Platforms through Dynamic Record Lifecycle Strategies
Keywords:
Cloud cost optimization, farm credit systems, record lifecycle management, serverless computingAbstract
Operational expenditure (OpEx) in cloud-based agricultural finance platforms has become a critical concern due to exponential growth in digital loan processing, remote data repositories, and compliance-driven record retention requirements. Farm credit management systems generate heterogeneous datasets including loan histories, satellite-based crop data, repayment logs, and risk assessment models, all of which must be stored, processed, and retained under regulatory constraints. This paper proposes a dynamic record lifecycle optimization framework aimed at minimizing operational costs in remote computing repositories while ensuring data accessibility, compliance, and system performance.
The study synthesizes adaptive storage governance principles, serverless computing paradigms, and multi-objective optimization techniques to design a cost-aware archival strategy. The framework integrates intelligent retention policies that dynamically classify, migrate, and archive financial records based on usage frequency, compliance priority, and storage cost gradients. Prior research highlights that cloud environments suffer from inefficiencies in static retention policies, leading to unnecessary storage expansion and increased financial overhead (Raghu & Chakravartula, 2025).
By incorporating adaptive lifecycle scheduling and workload-aware classification, the proposed model reduces redundant storage operations and improves retrieval efficiency. The system leverages insights from serverless computing architectures and cloud optimization strategies to ensure scalability and elasticity under fluctuating agricultural loan workloads. Comparative evaluation indicates that dynamic archival governance significantly reduces long-term storage costs while maintaining regulatory compliance thresholds.
The findings demonstrate that intelligent record lifecycle management can reduce operational expenditure in farm credit platforms by optimizing storage tier utilization, reducing cold data retention costs, and improving system-level efficiency. The paper contributes a structured architectural model and optimization strategy suitable for large-scale agricultural financial ecosystems.
References
1. A. Rashid and A. Chaturvedi, “Cloud computing characteristics and services: a brief review,” International Journal of Computer Sciences and Engineering, vol. 7, no. 2, pp. 421–426, 2019.
2. Bauer, André, Maxime Gonthier, Haochen Pan, Ryan Chard, Daniel Grzenda, Martin Straesser, J. Gregory Pauloski et al. “An Empirical Investigation of Container Building Strategies and Warm Times to Reduce Cold Starts in Scientific Computing Serverless Functions.” in 2024 IEEE 20th International Conference on e-Science (e-Science), pp. 1–10. IEEE, 2024.
3. Castro, Paul, Vatche Ishakian, Vinod Muthusamy, and Aleksander Slominski. “The server is dead, long live the server: Rise of Serverless Computing, Overview of Current State and Future Trends in Research and Industry.” arXiv preprint arXiv:1906.02888 (2019).
4. H. B. Hassan, S. A. Barakat, and Q. I. Sarhan, “A survey on serverless computing trends and research directions,” Journal of Cloud Computing, vol. 10, pp. 1–29, 2021.
5. Hassan, Hassan B., Saman A. Barakat, and Qusay I. Sarhan. “Survey on serverless computing.” Journal of Cloud Computing 10, no. 1 (2021): 39.
6. I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The rise of “big data” on cloud computing: Review and open research issues,” Information systems, vol. 47, pp. 98–115, 2015.
7. J. W. Heo, G. S. Ramachandran, A. Dorri, and R. Jurdak, “Blockchain data storage optimisations: a comprehensive survey,” ACM Computing Surveys, vol. 56, no. 7, pp. 1–27, 2024.
8. M. Al-Ayyoub, Y. Jararweh, M. Daraghmeh, and Q. Althebyan, “Dynamic provisioning and monitoring for cloud systems using a multi-agent approach,” Cluster Computing, vol. 18, pp. 919–932, 2015.
9. M. Daraghmeh, A. Agarwal, and Y. Jararweh, “Ensemble clustering framework for modeling hidden perspectives in cloud workload categorization,” Cluster Computing, vol. 27, no. 4, pp. 4779–4803, 2024.
10. M. Hosseinzadeh, M. Y. Ghafour, H. K. Hama, B. Vo, and A. Khoshnevis, “Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review,” Journal of Grid Computing, vol. 18, no. 3, pp. 327–356, 2020.
11. M. Ojha, K. P. Singh, P. Chakraborty, and S. Verma, “A review of multi-objective optimisation and decision making using evolutionary algorithms,” International Journal of Bio-Inspired Computation, vol. 14, no. 2, pp. 69–84, 2019.
12. P. Wang, C. Zhao, W. Liu, Z. Chen, and Z. Zhang, “Optimizing data placement for cost effective and high available multi-cloud storage,” Computing and Informatics, vol. 39, no. 1-2, pp. 51–82, 2020.
13. P. Yang, N. Xiong, and J. Ren, “Data security and privacy protection for cloud storage: A survey,” IEEE Access, vol. 8, pp. 131723–131740, 2020.
14. S. Farzai, M. H. Shirvani, and M. Rabbani, “Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters,” Sustainable Computing: Informatics and Systems, vol. 28, p. 100374, 2020.
15. S. Gupta and S. Tripathi, “A comprehensive survey on cloud computing scheduling techniques,” Multimedia Tools and Applications, vol. 83, no. 18, pp. 53581–53634, 2024.
16. S. Sharma and V. Kumar, “A comprehensive review on multi-objective optimization techniques: Past, present and future,” Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 5605–5633, 2022.
17. Tong, Zhao, Jiake Wang, Jing Mei, Kenli Li, and Keqin Li. “FedTO: Mobile-aware task offloading in multi-base station collaborative MEC.” IEEE Transactions on Vehicular Technology 73, no. 3 (2023): 4352–4365.
18. Wen, Jinfeng, Zhenpeng Chen, Xin Jin, and Xuanzhe Liu. “Rise of the planet of serverless computing: A systematic review.” ACM Transactions on Software Engineering and Methodology 32, no. 5 (2023): 1–61.
19. X. Xu, S. Fu, W. Li, F. Dai, H. Gao, and V. Chang, “Multi-objective data placement for workflow management in cloud infrastructure using NSGA-II,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 5, pp. 605–615, 2020.
20. P. Castro, Vatche Ishakian, Vinod Muthusamy, and Aleksander Slominski. “The server is dead, long live the server: Rise of Serverless Computing, Overview of Current State and Future Trends in Research and Industry.” arXiv preprint arXiv:1906.02888 (2019).
21. K. N. Chakravartula and A. Raghu, "Reducing Cloud Storage Costs in Agri-Lending CRM Systems Using Intelligent Data Retention Policies," 2025 8th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI), Nanjing, China, 2025, pp. 1–9, doi: 10.1109/ACAI68217.2025.11406232.0.1109/ACAI68217.2025.11406232.
