Recent research in resource-demand-based allocation in cloud computing emphasizes aligning resource provisioning dynamically with application demand profiles to optimize utilization, cost, and performance. These studies leverage predictive analytics, machine learning, and reinforcement learning techniques to forecast workload demand and allocate resources accordingly—ensuring that VMs, containers or storage are efficiently matched to current and anticipated demand spikes. The focus is on reducing over-provisioning and under?utilization by responding in real time to demand patterns and user requirements, thus enabling cloud infrastructures to deliver better scalability, sustainability and Quality of Service (QoS) across heterogeneous and multi?tenant environments.