Service Function Chain Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

Xu Feng, Mengyang He, Lei Zhuang (), Yanrui Song and Rumeng Peng
Additional contact information
Xu Feng: The School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Mengyang He: The School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Lei Zhuang: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
Yanrui Song: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
Rumeng Peng: The School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China

Future Internet, 2024, vol. 16, issue 1, 1-20

Abstract: SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

Keywords: space–air–ground integrated network; DRL; resource allocation; NFV (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/1/27/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/1/27/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:1:p:27-:d:1319887

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().