Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services
The growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network func...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/19/2976 |
| _version_ | 1797479969642774528 |
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| author | Prohim Tam Sa Math Seokhoon Kim |
| author_facet | Prohim Tam Sa Math Seokhoon Kim |
| author_sort | Prohim Tam |
| collection | DOAJ |
| description | The growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network function virtualization (NFV)-enabled environment. In IoT networks, the configuration of adaptive SFC has emerged to ensure optimality and elasticity of resource expenditure. In this paper, priority-aware resource management for adaptive SFC is provided by modeling the configuration of real-time IoT service requests. The problem models of the primary features that impact the optimization of configuration times and resource utilization are studied. The proposed approaches query the promising embedded deep reinforcement learning engine in the management layer (e.g., orchestrator) to observe the state features of VNFs, apply the action on instantiating and modifying new/created VNFs, and evaluate the average transmission delays for end-to-end IoT services. In the embedded SFC procedures, the agent formulates the function approximator for scoring the existing chain performance metrics. The testbed simulation was conducted in SDN/NFV topologies and captured the average of rewards, delays, delivery ratio, and throughput as −48.6666, 10.9766 ms, 99.9221%, and 615.8441 Mbps, which outperformed other reference approaches, following parameter configuration in this environment. |
| first_indexed | 2024-03-09T21:53:22Z |
| format | Article |
| id | doaj.art-fe585d5bfaba4d25891bd04e26493180 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:53:22Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-fe585d5bfaba4d25891bd04e264931802023-11-23T20:04:14ZengMDPI AGElectronics2079-92922022-09-011119297610.3390/electronics11192976Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT ServicesProhim Tam0Sa Math1Seokhoon Kim2Department of Software Convergence, Soonchunhyang University, Asan 31538, KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, KoreaThe growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network function virtualization (NFV)-enabled environment. In IoT networks, the configuration of adaptive SFC has emerged to ensure optimality and elasticity of resource expenditure. In this paper, priority-aware resource management for adaptive SFC is provided by modeling the configuration of real-time IoT service requests. The problem models of the primary features that impact the optimization of configuration times and resource utilization are studied. The proposed approaches query the promising embedded deep reinforcement learning engine in the management layer (e.g., orchestrator) to observe the state features of VNFs, apply the action on instantiating and modifying new/created VNFs, and evaluate the average transmission delays for end-to-end IoT services. In the embedded SFC procedures, the agent formulates the function approximator for scoring the existing chain performance metrics. The testbed simulation was conducted in SDN/NFV topologies and captured the average of rewards, delays, delivery ratio, and throughput as −48.6666, 10.9766 ms, 99.9221%, and 615.8441 Mbps, which outperformed other reference approaches, following parameter configuration in this environment.https://www.mdpi.com/2079-9292/11/19/2976deep reinforcement learningpriority-aware orchestrationservice function chainingsoftware-defined networkingvirtual network functions |
| spellingShingle | Prohim Tam Sa Math Seokhoon Kim Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services Electronics deep reinforcement learning priority-aware orchestration service function chaining software-defined networking virtual network functions |
| title | Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services |
| title_full | Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services |
| title_fullStr | Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services |
| title_full_unstemmed | Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services |
| title_short | Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services |
| title_sort | priority aware resource management for adaptive service function chaining in real time intelligent iot services |
| topic | deep reinforcement learning priority-aware orchestration service function chaining software-defined networking virtual network functions |
| url | https://www.mdpi.com/2079-9292/11/19/2976 |
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