• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing


Seyha Ros, Prohim Tam, Seokhoon Kim, Journal of Internet Computing and Services, Vol. 23, No. 5, pp. 17-23, Oct. 2022
10.7472/jksii.2022.23.5.17, Full Text:
Keywords: Deep Reinforcement Learning, Network Slicing, Software-Defined Networking, Network Functions Virtualization, Edge Computing

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.


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Cite this article
[APA Style]
Ros, S., Tam, P., & Kim, S. (2022). Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing. Journal of Internet Computing and Services, 23(5), 17-23. DOI: 10.7472/jksii.2022.23.5.17.

[IEEE Style]
S. Ros, P. Tam, S. Kim, "Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing," Journal of Internet Computing and Services, vol. 23, no. 5, pp. 17-23, 2022. DOI: 10.7472/jksii.2022.23.5.17.

[ACM Style]
Seyha Ros, Prohim Tam, and Seokhoon Kim. 2022. Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing. Journal of Internet Computing and Services, 23, 5, (2022), 17-23. DOI: 10.7472/jksii.2022.23.5.17.