Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A game-theoretic model for enterprises cloud computational resource allocation based on predefined-time distributed algorithm. |
| Συγγραφείς: |
Gao, Ying1,2 (AUTHOR), Chen, Jianing2,3 (AUTHOR), Fang, Yue1,4 (AUTHOR), Dang, Chuangyin2 (AUTHOR), Su, Qin1 (AUTHOR) qinsu@mail.xjtu.edu.cn |
| Πηγή: |
IISE Transactions. Aug2026, Vol. 58 Issue 8, p915-933. 19p. |
| Θεματικοί όροι: |
*Resource allocation, *Distributed algorithms, *Cloud computing, *Manufacturing industries, *Game theory, *Nash equilibrium, *Programming languages, *Resource management |
| People: |
Nash, John F., 1928-2015 |
| Περίληψη: |
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. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: |
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