Research in Cloud Usage Patterns focuses on understanding and modeling how users and applications interact with cloud resources to optimize performance, cost, and scalability. This area explores identifying recurring patterns in workload behavior, resource consumption, and application deployment to improve cloud service management and prediction accuracy. Key research directions include workload characterization and classification, dynamic resource allocation based on usage trends, anomaly detection in cloud utilization, and forecasting demand using machine learning techniques. Other topics include developing adaptive scheduling algorithms informed by usage analytics, identifying cost-optimization strategies through usage modeling, and designing intelligent monitoring systems that learn from historical cloud usage data to support proactive management and sustainability in large-scale cloud infrastructures.