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AI-driven Scaling Strategies for Adaptive Workload Management in Distributed Cloud Systems

Author(s) Kalesha Khan Pattan
Country Malaysia
Abstract In distributed cloud systems, managing workloads efficiently under unpredictable demand variations remains a critical challenge. Conventional auto-scaling mechanisms, which depend on static thresholds or reactive triggers, often result in delayed responses and inefficient resource utilization. This research introduces an AI-driven adaptive scaling framework that leverages machine learning to predict workload fluctuations and dynamically reconfigure resource allocation in real time. The central proof of this study is established through significant improvement in system response time, demonstrating that AI-based scaling ensures optimal performance and stability across diverse workloads. The proposed framework integrates reinforcement learning and predictive analytics to continuously monitor metrics such as CPU utilization, latency, and system load, enabling proactive scaling decisions before performance degradation occurs. Unlike traditional methods that react after congestion or underutilization is detected, the AI-driven approach learns workload behavior patterns and performs predictive scaling actions. Experiments were conducted on clustered environments of varying sizes, ranging from three to eleven nodes, under three distinct workload categories: static web requests, database-intensive queries, and mixed computational loads. The evaluation results confirm that response time consistently improved by 35 to 44 percent across all workloads compared to rule-based scaling. This improvement directly reflects the framework’s ability to anticipate scaling needs, minimize queuing delays, and maintain service continuity during fluctuating workloads. The adaptive scaling model not only enhances responsiveness but also reduces over-provisioning and energy consumption, leading to sustainable and cost-effective operations. By presenting response time as the primary validation metric, this study provides empirical proof that AI-driven scaling significantly enhances workload management in distributed cloud systems. The research concludes that adaptive learning-based strategies outperform static approaches in both responsiveness and resource optimization. Future work will extend this model toward multi-cloud orchestration, energy-aware scaling, and federated reinforcement learning for intelligent workload balancing across globally distributed infrastructures.
Field Engineering
Published In Volume 13, Issue 5, Array 2022
Published On 2022-05-07
Cite This AI-driven Scaling Strategies for Adaptive Workload Management in Distributed Cloud Systems - Kalesha Khan Pattan - IJTAS Volume 13, Issue 5, Array 2022. DOI 10.71097/IJTAS.v13.i5.1125
DOI https://doi.org/10.71097/IJTAS.v13.i5.1125
Short DOI https://doi.org/g98f4v
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