Research on Resource Demand-based Allocation in Cloud Computing focuses on designing strategies that allocate cloud resources dynamically based on real-time or predicted workload demands, aiming to optimize performance, cost, and resource utilization. This area explores methods to monitor application requirements, forecast future demands, and adaptively distribute computational, storage, and network resources. Key research directions include demand-aware scheduling algorithms, predictive resource provisioning using machine learning, and SLA-compliant allocation policies. Other emerging topics involve energy-efficient demand-based allocation, multi-objective optimization balancing QoS, cost, and utilization, and container- or VM-level orchestration for elastic resource management. Additionally, integrating edge and fog resources, developing fault-tolerant and adaptive allocation frameworks, and applying reinforcement learning or heuristic-based approaches for real-time demand-driven resource allocation are significant avenues for advancing intelligent and efficient cloud computing infrastructures.