ABSTRACT Cloud computing offers on‐demand access to computing resources; however, minimizing response time while ensuring compliance with Service Level Agreements (SLAs) remains a critical challenge. The proposed CLOUD SMART framework aims to intelligently minimize response time, process delays, and propagation latency in cloud environments through adaptive, SLA‐aware dynamic scheduling. This study evaluates the model using the Cloud Workload Dataset for Scheduling Analysis, available on Kaggle. The dataset undergoes preprocessing, including median imputation for missing numerical values, and the derivation of key features such as response time, deadlines, and priority tiers for accurate workload profiling. Workload characterization through statistical profiling and clustering reveals patterns, arrival rates, and task categories that guide scheduling strategy selection. Baseline performance is established using discrete event simulation of standard policies such as FCFS, SJF/Min‐Min, Max‐Min, and EDF. A novel Predictive Deadline‐Aware Hybrid Scheduling (PDHS) approach is integrated to predict completion times and dynamically switch scheduling strategies based on urgency. An execution and closed‐loop feedback mechanism enables real‐time adaptation. Experimental results show that CLOUD SMART significantly reduces response time, improves SLA compliance, and enhances resource utilization compared to static scheduling baselines. The PDHS model achieves an average response time of 6.72 s, significantly lower than all baselines. Average waiting time is reduced to 2.87 s, and Makespan improves to 138.4 s. SLA compliance reaches 97%, with a deadline miss ratio of only 3%. System throughput is enhanced to 38.5 tasks per second, and resource utilization climbs to 92%. Prediction accuracy excels with an MAE of 0.94 s and RMSE of 1.26 s.
Anuradha et al. (Thu,) studied this question.