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Gil Jiménez, VP, Al-Jumaily, A, Sali, A and Al-Jumeily, D (2023) Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation. Journal of Autonomous Intelligence, 6 (2). p. 652.
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Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (683kB) | Preview |
Abstract
To enhance the smart Cloud Service System for diverse user requirements in 5G and other service networks, this study leverages resource utilization and multi-tenancy network slicing operation costs. Specifically, we propose a multi-tenancy network resource allocation strategy based on the Chaos Particle Swarm Optimization (CPSO) algorithm. In a multi-tenancy network (MTN), we lease the wireless spectrum resources of the infrastructure provider’s base station, construct access service slices as network slice services, and offer network access services to users. Introduce detailed formulation of the relationship between MTN and users, represented as a multi-master and multi-slave construct that defines the strategy space and profit function after MTN decision-making. Reverse induction is used to analyze the proposed model, and a distributed iterative algorithm is proposed to obtain the optimal throughput demand of users and the optimal slice cost of MTN. Simulation results demonstrate that the proposed strategy can effectively enhance resource utilization and user satisfaction while reducing energy consumption.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Frontier Scientific Publishing |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 22 Nov 2023 16:34 |
| Last Modified: | 22 Nov 2023 16:45 |
| DOI or ID number: | 10.32629/jai.v6i2.652 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/21923 |
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