An optimal three-tier prioritization-based multiflow scheduling in cloud-assisted smart healthcare

• Published in: Journal of network and computer applications Vol. 238; p. 104143
• Main Authors: Sarthak, Verma, Anshul, Verma, Pradeepika
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2025
• Subjects: Cloud resource allocation; Healthcare service prioritization; Heuristics; MCDM; Smart healthcare; Task scheduling; Workflow optimization; Healthcare service prioritization; Workflow optimization; Smart healthcare; Heuristics; Task scheduling; MCDM; Cloud resource allocation
• ISSN: 1084-8045
• DOI: 10.1016/j.jnca.2025.104143

Internet of Things is significantly advancing the development of modern interconnected networks. Coordinated with cloud computing, this technology becomes even more powerful, cost-effective, and reliable. These advancements are rapidly being integrated into modern healthcare through innovations such as smart ambulances, remote monitoring systems, and smart hospitals, enhancing tracking, analysis, and alerting capabilities. However, these innovations also bring challenges, particularly in task allocation and resource management for safety-critical systems that must meet stringent quality of service while efficiently utilizing resources. This paper introduces a new heuristic Three-Tier Prioritization based Multiflow Scheduling (TTPMS) approach for smart healthcare in cloud, utilizing the adaptive multi-criteria decision-making. The proposed TTPMS algorithm prioritizes tasks across three levels, considering factors such as urgency, deadlines, budget, and impact value within the workflow, and then dynamically selects the most suitable virtual machine for allocation. Performance comparisons were made against traditional approaches like the Prioritized Sorted Task-Based Algorithm (PSTBA) and the Max–Min algorithm. Experiments conducted using the Eclipse IDE with Java, demonstrated that the proposed approach significantly outperforms traditional algorithms across multiple metrics, including success rates for deadlines and budgets, as well as the resource utilization. It achieved a 98% deadline adherence rate, outperforming Max–Min (93%) and PSTBA (60%). Additionally, TTPMS surpassed budget adherence metrics, achieving a 76% success rate compared to PSTBA (72%) and Max–Min (70%). For combined adherence to both deadlines and budgets, TTPMS achieved a 74% success rate, outperforming PSTBA (33%) and Max–Min (63%). These results highlight the effectiveness of TTPMS in scheduling the healthcare applications.