Efficient Orchestration of Virtualization Resource in RAN Based on Chemical Reaction Optimization and Q-Learning
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Internet of Things Journal, 2022, 9, (5), pp. 3383-3396
- Issue Date:
- 2022-03-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Efficient_Orchestration_of_Virtualization_Resource_in_RAN_Based_on_Chemical_Reaction_Optimization_and_Q-Learning.pdf | Published version | 2.53 MB |
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Virtualized network function (VNF) orchestration dynamically deploys network slices, which provides an effective means of customized service provision. To achieve a realistic and comprehensive perspective of the decision process for customized service provision, we propose a virtualized resource orchestration strategy in the radio access network (RAN) of Internet of Things (IoT) based on chemical reaction optimization (CRO). Specifically, we apply particle swarm optimization (PSO), a Gaussian process, random walk model, and Q -learning to enhance the CRO algorithm to quickly obtain the approximate optimal solution for the proposed CRO-based resource orchestration strategy (CROROS). The simulation results show that compared with existing access methods, CROROS can reduce the service rejection rate of a virtualized RAN and improve the utilization rate of network system resources. Compared with other heuristic algorithms [e.g., PSO, genetic algorithm (GA), and CRO], CROROS can accelerate the global approximate optimal solution and improve the approximate fitness of the approximate optimal solution within a specified time.
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