This journal is published in the domestic journal of Journal of Internet Computing and Services (인터넷정보학회논문지)
Volume 23 Issue 5- /
- Pages.17-23
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- 2022
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- 1598-0170(pISSN)
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Korean Society for Internet Information (한국인터넷정보학회)
Ros Seyha (Department of Software Convergence, Soonchunhyang University) Tam, Prohim (Department of Software Convergence, Soonchunhyang University) ;Kim, Seokhoon (Department of Software Convergence, Soonchunhyang University)
- 로스세이하
- 담프로힘 ;
- 김석훈
- Cite Here !
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 captures the QoS performances between conventional and MDRL-A approaches.
Keywords
- Deep reinforcement learning;
- network slicing;
- software-defined networking;
- network functions virtualization;
- edge computing
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