Research on Deadline-Constrained Scheduling in Cloud Computing focuses on designing algorithms that ensure timely task execution while optimizing resource utilization and energy efficiency. Recent advancements include methods like the Deadline-DDEP algorithm, which dynamically adjusts sub-deadlines based on the evolving essential path of tasks to meet complex workflow requirements, and energy-aware models that prioritize tasks considering both deadlines and energy consumption to enhance overall system efficiency. Comparative studies of multiple deadline-constrained scheduling approaches provide insights into their performance, highlighting the importance of integrating deadline awareness into cloud scheduling frameworks to balance timely execution, resource management, and energy optimization.