Optimizing Task Scheduling in Cloud based Healthcare System using Adaptive Parameter Control Reptile Search Algorithm

doi:10.1080/02564602.2026.2615339

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Optimizing Task Scheduling in Cloud based Healthcare System using Adaptive Parameter Control Reptile Search Algorithm

2026-01-01

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Ummay Rumman Tahira

Anshul Verma

Pradeepika Verma

DOI：10.1080/02564602.2026.2615339

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Cloud Computing and the Internet of Things play pivotal roles in advancing the healthcare system through enhanced observation mechanisms. These mechanisms can be implemented using various algorithms, including the Sparrow Search Algorithm (SSA), Goal Programming Algorithm (GPA) and Reptile Search Algorithm (RSA) etc. Task scheduling is one of the major challenges in cloud computing, as solving this problem requires reducing costs while meeting deadlines. Efficient task scheduling is essential to optimize resource utilization and ensure timely completion of tasks. To overcome this challenge, this study presents an innovative algorithm named Adaptive Parameter Control Reptile Search Algorithm (APC-RSA) which is designed to optimize healthcare tasks scheduling by achieving a balance between time and cost as well as ensuring tasks completion within deadlines. The performance of APC-RSA is driven by dynamic parameter adjustment that balances exploration and exploitation during optimization. This study evaluates the effectiveness of APC-RSA demonstrating the significant improvements over existing algorithms like SSA, GPA and RSA. Experimental results indicate that APC-RSA achieves a minimized cost of 17,061.68 with a 99.83% task success rate. In comparison, RSA achieves a cost of 45,821.17 with a 99.70% success rate, SSA achieves a cost of 50,411.52 with a 99.67% success rate, and GPA incurs a cost of 138,448.73 with a 95.37% task success rate. The findings suggest that APC-RSA has the potential to significantly enhance cloud task scheduling in the healthcare sector, offering a cost-effective and reliable solution to improve global healthcare systems.

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