Cloud-Based Replication Models Using AI Techniques for Enhanced Data Management

Abstract: Elastic cloud infrastructure relies on dynamic replication mechanisms to maintain service availability and performance under fluctuating, non-stationary workloads. However, conventional threshold-based and static replication strategies frequently fail to maintain latency stability and Service Level Agreement (SLA) compliance in highly dynamic environments characterised by bursty, peak-stress traffic. This study introduces a Q-learning–based adaptive replication framework that formulates replication control as a sequential decision-making problem. The system is modelled as a Markov Decision Process (MDP), where replication adjustments are selected to maximise cumulative discounted reward, integrating latency minimisation, SLA violation penalties, and replica cost regularisation within a unified optimisation objective. A controlled cloud simulation environment was developed to emulate phased stochastic workload patterns, including normal, burst, sustained peak, and recovery intervals. The reinforcement learning controller was trained over 5000 episodes and subsequently evaluated under fixed-policy conditions against a reaction-delayed rule-based baseline controller. Experimental results demonstrate substantial improvements in performance stability. The proposed learning-based controller achieves a significant reduction in average latency, strong suppression of 95th percentile tail latency, and complete elimination of SLA violations under dynamic workload conditions. Unlike reactive threshold-based mechanisms, the learned policy anticipates workload transitions and proactively adjusts replication levels through long-term reward optimisation. These findings confirm that learning-driven replication control provides a structurally superior paradigm for latency-sensitive elastic cloud systems. By embedding SLA awareness directly into the reward formulation, replication management is transformed from a static configuration task into an adaptive, intelligent control process.

Moneef M. Jazzar and Aws I. Abueid. “Cloud-Based Replication Models Using AI Techniques for Enhanced Data Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170346

@article{Jazzar2026,
title = {Cloud-Based Replication Models Using AI Techniques for Enhanced Data Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170346},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170346},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Moneef M. Jazzar and Aws I. Abueid}
}