Cloud–Edge Collaborative SFC Mapping for Industrial IoT Using Deep Reinforcement Learning
| Autor: | Li Yimin, Xuesong Qiu, Shaoyong Guo, Siya Xu, Chenghao Lei, Di Liu |
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| Rok vydání: | 2022 |
| Předmět: |
Service (systems architecture)
Mobile edge computing Computer science business.industry Distributed computing Cloud computing Computer Science Applications Control and Systems Engineering Software deployment Key (cryptography) Reinforcement learning Enhanced Data Rates for GSM Evolution Electrical and Electronic Engineering business 5G Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 18:4158-4168 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2021.3113875 |
| Popis: | The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization technology with cloud computing and mobile edge computing, an NFV-enabled cloud-edge collaborative IIoT (CECIIoT) architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, though edge and cloud resources can be jointly utilized in this architecture, it is hard to balance resource consumption and the quality of multiple IIoT services. To overcome this challenge, a multi-objective SFC deployment model is designed to characterize the diverse service requirements and specific network environment. Then, a deep Q-learning based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training.The simulation results validate the execution effectiveness. |
| Databáze: | OpenAIRE |
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