Autonomous Capacity Provisioning and Real-Time Traffic Coordination for Robust and Economical Cloud-Based Service Infrastructures
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Abstract
Cloud service systems have become a foundational infrastructure for modern digital applications, supporting large-scale data processing, distributed storage, online platforms, and intelligent computing services. However, cloud environments often face challenges such as fluctuating workloads, uneven resource utilization, service interruptions, and high operational costs. This paper investigates adaptive resource management for reliable and cost-efficient cloud service systems. We propose a dynamic cloud resource optimization framework that integrates workload monitoring, demand prediction, resource allocation, and feedback-based adjustment. The framework analyzes runtime service conditions and automatically adjusts computing resources to improve system stability, reduce resource waste, and maintain efficient service delivery under changing workload patterns. By coordinating task placement, load distribution, and resource scaling, the proposed approach enhances the overall reliability and operational efficiency of cloud service platforms. Experimental results show that the framework improves resource utilization, reduces average response latency, and lowers operational cost compared with static allocation and conventional threshold-based scaling methods. These findings demonstrate that cloud service optimization requires continuous runtime adaptation rather than fixed resource provisioning. This study provides a practical approach for building scalable, resilient, and cost-effective cloud service systems in dynamic computing environments.
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