Controlled Service Scheduling Scheme for User-Centric Software-Defined Network- Based Internet of Things
Software Defined Networks (SDNs) support different applications’ data and control operations through operational plane differentiations. Such differentiations rely on the service providers’ user density and processing capacity. This article introduces a Controlled Service Sched...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10851301/ |
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| Summary: | Software Defined Networks (SDNs) support different applications’ data and control operations through operational plane differentiations. Such differentiations rely on the service providers’ user density and processing capacity. This article introduces a Controlled Service Scheduling Scheme (CS3) to ensure responsive user service support. This scheme exploits the SDN’s operation plane differentiation to confine immobile request stagnancies. The routed regression learning model decides the SDN plane selection. This learning is a modified version of linear learning where the scheduling rate is the plane differentiator. The process is un-iterated until the combination of device processing capacity and number of devices is less than the service population observed. In the scheduling process, the operation to data plane migrations is decided using the maximum routed threshold. The threshold is computed for the operation and data plane from which the rate of service response or capacity of service admittance is decided. The routed regression analyzes the change in the threshold factor to ensure flexible scheduling is achieved regardless of dense IoT requests. This scheme achieves a high scheduling rate for maximizing service distributions under controlled delay. The experimental findings show that compared to the current models, the suggested method improves the scheduling rate by 13.92%, increases the distribution of services by 8.31%, and decreases delays by 11.58%. Further evidence of the approach’s efficacy in managing heavy IoT traffic is its low distribution failure rate of 1.7%. These findings demonstrate that the scheme can enhance performance in ever-changing Internet of Things settings by optimizing the allocation of resources. |
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| ISSN: | 2169-3536 |