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CHPSO: An Efficient Algorithm for Task Scheduling and Optimizing Resource Utilization in the Cloud Environment
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AI
DOI：10.1007/s10723-025-09803-8.png)
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摘要
En 中文
In cloud computing, efficient task scheduling is crucial for optimizing system performance and resource utilization. Addressing this, we introduce a novel algorithm designed to enhance task distribution across virtual servers based on their processing capabilities. Leveraging the total MIPS metric, the CHPSO algorithm (Chi-squared Particle Swarm Optimization) provides a comprehensive assessment of computational capacity, which facilitates strategic task allocation to improve the efficiency of cloud-based applications. Our method employs a two-pronged approach: firstly, it models task arrival times using a chi-squared distribution, which creates realistic workload scenarios that reflect variable arrival rates. Secondly, it adopts a heuristic strategy that balances the workload across VSs by considering their cumulative processing times. Additionally, we integrate a PSO algorithm to refine task-to-VS mapping, ensuring optimal task placement. The effectiveness of our algorithm is evaluated using key metrics such as response time, energy consumption, makespan, execution time, and resource utilization. Compared to existing state-of-the-art algorithms, CHPSO demonstrates significant improvements in these metrics, showcasing its capability to manage dynamic and complex environments in cloud computing. Our results indicate that CHPSO not only optimizes performance but also enhances resource efficiency, making it a valuable contribution to the field.
Keyword:
Cloud computing
Task scheduling
Resource utilization
Makespan
Energy consumption
