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A game-theoretic model for enterprises cloud computational resource allocation based on predefined-time distributed algorithm
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AI
DOI：10.1080/24725854.2025.2561570.png)
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Abstract
En 中文
In the era of smart manufacturing, the way enterprises handle tasks has been revolutionized by cloud computing, shifting from on-premises IT environments to the cloud. However, the competition among multiple enterprises for limited computational resources available at cloud service providers presents a significant challenge in fairly allocating resources to minimize response delays. To offer a reasonable allocation strategy for cloud service providers in a multi-enterprise cloud environment, this paper introduces a novel aggregative game model within a three-tier computational offloading architecture across local manufacturing devices, resource-constrained small base stations, and high-capacity cloud data centers. Due to the aggregation of enterprises' strategies, traditional evolutionary algorithms are infeasible to solve this game problem. Stemming from recurrent neural networks, a predefined-time distributed generalized Nash equilibrium seeking algorithm with event-triggered communication is proposed. From a communication perspective, the event-triggered distributed setting ensures that enterprises engage in discrete-time local communication, aligning with the practical, competitive and information-preserving nature of the enterprise interactions. From a computational perspective, the algorithm allows cloud service providers to autonomously balance computational efficiency and decision accuracy, with user-defined convergence times for decision updates. This enables the rapid determination of equilibrium strategies, providing cloud service providers with a scalable solution that was unattainable in previous methods.
Keywords:
Computational resource allocation
cloud computing
aggregative game
predefined-time convergence
event-triggered mechanism
distributed algorithm
Journal
IF:
2.3
Papers:
85
Citations:
1.9K
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