Research on Resource Allocation Optimization in Cloud Computing focuses on designing strategies and algorithms to efficiently distribute computational, storage, and network resources among competing users and applications while ensuring performance, cost-effectiveness, and Quality of Service (QoS). This area explores both static and dynamic allocation methods to handle varying workloads in heterogeneous and large-scale cloud environments. Key research directions include heuristic- and metaheuristic-based optimization techniques (e.g., genetic algorithms, particle swarm optimization, ant colony optimization), machine learning-driven predictive resource allocation, and energy- and cost-aware allocation strategies. Other emerging topics involve multi-objective optimization balancing QoS, energy consumption, and resource utilization, SLA-compliant allocation policies, container- and VM-level orchestration, and adaptive resource management in cloud–edge integrated environments. Additionally, research on fault-tolerant, scalable, and real-time allocation frameworks, as well as incentive-based and game-theoretic approaches for fair and efficient resource sharing, represents significant avenues for advancing intelligent and sustainable cloud computing infrastructures.