A game-theoretic model for enterprises cloud computational resource allocation based on predefined-time distributed algorithm
针对多企业竞争云服务商有限计算资源的问题，提出一种三层计算卸载架构下的聚合博弈模型，并设计基于事件触发的预定义时间分布式算法，帮助云服务商快速公平分配资源、降低响应延迟。
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.