To Design the Adaptive Consistency via Machine-Learned Policy Control for Globally Distributed Databases
Nirmla Sharma1, Sameera Iqbal Muhmmad Iqbal2
1Dr. Nirmla Sharma, Assistant Professor, Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia,
2Sameera Iqbal Muhmmad Iqbal, Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia.
Manuscript received on 01 June 2026 | Revised Manuscript received on 09 June 2026 | Manuscript Accepted on 15 June 2026 | Manuscript published on 30 June 2026 | PP: 17-22 | Volume-15 Issue-5, June 2026 | Retrieval Number: 100.1/ijeat.F479315060826 | DOI: 10.35940/ijeat.F4793.15050626
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We have presented the adaptive consistency framework for globally distributed databases that uses a machine-learned policy controller to balance latency, throughput, and correctness under dynamic workloads. This approach has treated consistency as a tunable knob, guided by real-time observability, workload characteristics, and service-level objectives (SLOs). A lightweight supervisor has collected end-to-end latency, read/write latency distribution, and data staleness metrics, and has selected a consistency level (e.g., strong, bounded staleness, or eventual) at the operation granularity or per session. The policy has learned offline from historical traces and updated online via a safe incremental learning loop that avoids destabilizing the system. The objective of this research is the formalisation of adaptive consistency as a policy-optimisation problem with stability guarantees. A learnable controller that integrates latency, staleness, and throughput signals. Practical guidelines for deployment, monitoring, and safety are also provided. We have implemented the framework on top of a representative distributed database prototype and evaluated it under synthetic and real workloads, including flash crowds, skewed key access, and partial network partitions. The results show a reduction of up to 28.6% in tail latency (p95/p99) with controlled staleness deviation, and a 75% improvement in overall throughput under bursty conditions, compared to 20% with static consistency configurations. We have considered the organisational concerns, security requirements, and opportunities for integration with the current Database-as-a-Service (DBaaS) platform.
Keywords: Adaptive Consistency, Distributed Databases, Globally, Machine Learning, Service Level Agreement (SLA), Service Level Objectives (SLOs) and Policy Control.
Scope of the Article: Computer Science and Engineering