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Vol. 13 No. 03 (2026)
Volume 13 Issue 03

Articles

Expenditure Minimization across Distributed Computing Archives for Agricultural Finance Management Platforms via Adaptive Archival Governance Frameworks

Published 2026-03-24

  • Pulatova Nguyen Minh Tuan
    • ,
  • Le Thi Phuong Anh

Pulatova Nguyen Minh Tuan
Department of Artificial Intelligence and Data Engineering Hanoi University of Science and Technology Hanoi, Vietna

Le Thi Phuong Anh
Faculty of Computer Science and Cloud Systems Vietnam National University Ho Chi Minh City,Vietnam of Wellington

Abstract

The exponential growth of cloud-based agricultural finance management systems has introduced significant challenges in controlling long-term data storage expenditure across distributed computing environments. Agricultural lending platforms generate massive volumes of heterogeneous datasets, including loan records, repayment histories, satellite-based crop analytics, and customer interaction logs. Over time, these datasets accumulate in distributed archives, leading to inefficient storage utilization and escalating operational costs. This paper proposes a conceptual and analytical framework for expenditure minimization through Adaptive Archival Governance Frameworks (AAGF), specifically designed for agricultural finance ecosystems operating in distributed cloud infrastructures.

The proposed framework integrates intelligent data retention strategies, predictive access modeling, and cost-aware replication mechanisms to optimize storage allocation across multi-tier cloud environments. The design builds upon established cloud cost optimization principles and distributed storage models (Mansouri et al., 2017; Wu et al., 2013). A central contribution of this study is the incorporation of adaptive governance policies that dynamically regulate data lifecycle transitions based on predictive utility scores and financial relevance.

A key theoretical foundation of this work is derived from intelligent data retention mechanisms for agri-lending CRM systems, where adaptive lifecycle control significantly reduces storage overhead while preserving compliance requirements (Chakravartula and Raghu, 2025). This foundational study is extended in this research to a distributed multi-cloud archival context, enabling scalable cost optimization across heterogeneous infrastructures.

Furthermore, predictive intelligence models inspired by LSTM networks (Hochreiter and Schmidhuber, 1997) are utilized to estimate data access probability, enabling proactive archival migration. Compression and optimization strategies for cloud-based data systems (Hossain et al., 2019) are integrated to further reduce storage footprint. Simulation-based evaluation using cloud modeling frameworks such as CloudSim (Calheiros et al., 2011) demonstrates significant improvements in cost efficiency.

Results indicate that adaptive governance-based archival systems can reduce long-term storage expenditure while maintaining data availability and regulatory compliance in agricultural finance platforms. The study provides a scalable, AI-driven architectural direction for next-generation financial cloud systems.

Keywords

Cloud storage optimization, agricultural finance systems, distributed archives, adaptive governance

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