Research on Resource Utilization-based Scheduling and Allocation in cloud computing focuses on developing strategies that maximize the efficient use of computational, storage, and network resources while meeting performance, cost, and reliability requirements. This area explores methods to monitor, predict, and optimize resource usage to improve overall system throughput and reduce underutilization or wastage. Key research directions include workload-aware and utilization-driven scheduling algorithms, dynamic and adaptive resource allocation policies, and predictive modeling using machine learning to anticipate demand patterns. Other emerging topics involve energy-efficient scheduling, multi-objective optimization balancing QoS and resource consumption, container- and VM-level resource orchestration, and resource consolidation techniques. Additionally, integrating edge and fog computing resources, designing fault-tolerant and scalable allocation frameworks, and employing reinforcement learning or heuristic-based methods for intelligent, real-time resource management are significant avenues for research in enhancing cloud system efficiency.