Optimizing Task Scheduling in Cloud Computing with Deep Learning: A Diabetes Detection Case Study

Efficient task scheduling in GPU-enabled cloud computing environments remains a critical challenge, as optimizing resource utilization, minimizing execution time, and ensuring fair task distribution are essential. This paper proposes a new approach integrating heuristic scheduling algorithms, such as Round Robin (RR) and Shortest Job First (SJF), with deep learning models to enhance scheduling efficiency. The proposed approach is applied to a real-world healthcare application-diabetes detection-where deep learning models trained on medical datasets, demonstrate how efficient task scheduling in GPU-enabled cloud environments improves the performance of predictive healthcare systems. The experimental results showed that the Round Robin (RR) algorithm provides the most balanced scheduling strategy due to its fairness in job distribution, and its integration with deep learning leads to significant improvements in efficiency. Among the evaluated models, RR-GRU achieves the highest performance, with an accuracy of 97.13% and an F1-score of 80.21%. The hybrid RR-based deep learning models enhance task prioritization, reduce delays, and optimize overall system performance. These results highlight the potential of AI-driven scheduling in cloud environments and its applicability in critical domains such as healthcare, offering a scalable and efficient solution for resource-intensive applications.