한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing |
| 영문제목(English Title) |
Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing |
| 저자(Author) |
로스세이하
담프로힘
김석훈
Seyha Ros
Prohim Tam
Seokhoon Kim
|
| 원문수록처(Citation) |
VOL 23 NO. 05 PP. 0017 ~ 0023 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining(SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches. |
| 키워드(Keyword) |
Deep reinforcement learning
network slicing
software-defined networking
network functions virtualization
edge computing
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| 파일첨부 |
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