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Home / Archives / Volume-6 / Issue-4 / Article-6

Volume - 6 | Issue - 4 | december 2024

Advanced Task Scheduling in Cloud Healthcare Systems with Hybrid MFO-PSO and Artificial Bee Colony Optimization
Vijaykumar Mamidala  , Thirusubramanian Ganesan, Mohanarangan Veerappermal Devarajan, Akhil Raj Gaius Yallamelli, Rama Krishna Mani Kanta Yalla
Pages: 374-394
Cite this article
Mamidala, Vijaykumar, Thirusubramanian Ganesan, Mohanarangan Veerappermal Devarajan, Akhil Raj Gaius Yallamelli, and Rama Krishna Mani Kanta Yalla. "Advanced Task Scheduling in Cloud Healthcare Systems with Hybrid MFO-PSO and Artificial Bee Colony Optimization." Journal of IoT in Social, Mobile, Analytics, and Cloud 6, no. 4 (2024): 374-394
Published
08 February, 2025
Abstract

Efficient task scheduling in cloud healthcare systems is essential for handling large-scale data, optimizing resource utilization, and enhancing system performance. This research presents a hybrid optimization approach, MFO-PSO-ABC, integrating Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms to address the complexities of dynamic workloads and resource constraints. The proposed hybrid method demonstrates superior performance in accuracy, efficiency, and resource utilization compared to individual algorithms, significantly improving task scheduling and system adaptability in real-time cloud healthcare environments.

Keywords

Task Scheduling Cloud Healthcare MFO PSO ABC Hybrid Optimization Real-Time Adaptation Resource Management

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