Predictive Resource Allocation Using AI in Cloud Systems

Abstract

The dynamic nature of cloud computing workloads creates significant challenges for efficient resource allocation. Applications may experience unpredictable changes in CPU, memory, storage, and network requirements, making static allocation approaches inefficient. Artificial Intelligence (AI) and machine learning techniques can address this challenge by predicting future resource requirements and enabling proactive allocation. This paper presents a predictive resource allocation approach using AI in cloud computing systems. The proposed framework collects historical and real-time workload information, analyzes resource utilization patterns, predicts future demand, and dynamically allocates resources according to predicted requirements. The objective is to reduce resource wastage, improve utilization, and maintain application performance during changing workload conditions. An experimental evaluation can be conducted using simulated cloud workloads representing different demand patterns. The expected results indicate that predictive allocation can reduce unnecessary resource provisioning and improve response time compared with conventional reactive approaches. The study demonstrates the potential of AI-based prediction for developing efficient and adaptive cloud resource management systems.