Research on Deadline-aware Workflow Scheduling in Cloud Computing focuses on designing strategies to schedule tasks and workflows in a manner that ensures completion within user-specified deadlines while optimizing resource utilization, cost, and performance. This area addresses the challenges of dynamic and heterogeneous cloud environments, where workloads vary and resources are shared among multiple users. Key research directions include deadline-constrained scheduling algorithms using heuristics, metaheuristics (e.g., genetic algorithms, particle swarm optimization, ant colony optimization), and hybrid optimization techniques to balance execution time, cost, and energy efficiency. Other emerging topics involve adaptive and real-time deadline-aware scheduling frameworks, cloud–edge integrated workflow scheduling for latency-sensitive applications, and multi-objective optimization that simultaneously considers makespan, cost, and QoS metrics. Additionally, research on SLA-compliant deadline management, fault-tolerant scheduling under resource failures, and predictive or machine learning-based approaches for proactive deadline adherence represents significant avenues for advancing intelligent and reliable cloud workflow management.