QoS Aware Virtual Network Embedding in Space-Air-Ground-Ocean Integrated Network

被引:1
|
作者
Zhang, Yi [1 ,2 ]
Zhang, Peiying [1 ,2 ,3 ]
Jiang, Chunxiao [4 ]
Wang, Shangguang [5 ]
Zhang, Hongxia [1 ]
Rong, Chunming [6 ]
机构
[1] China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
[2] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[3] Qilu Univ Technol, Shandong Acad Sci, Shandong Comp Sci Ctr, Natl Supercomp Ctr Jinan,Minist Educ,Key Lab Comp, Jinan 250013, Peoples R China
[4] Tsinghua Univ, Tsinghua Space Ctr, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Beijing 100084, Peoples R China
[5] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[6] Univ Stavanger, Dept Elect Engn & Comp Sci, N-4036 Stavanger, Norway
基金
中国国家自然科学基金;
关键词
Satellites; Quality of service; Network architecture; Delays; Heterogeneous networks; Heuristic algorithms; Space-air-ground-ocean Integrated Network; Software Defined Network; Virtual Network Architecture; Virtual Network Embedding; Reinforcement Learning; BANDWIDTH; SERVICE;
D O I
10.1109/TSC.2024.3357707
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The space-air-ground-ocean integrated network (SAGOI-Net) has become the focus of research in recent years, which has the characteristics of wide coverage and strong adaptability. However, due to the influence of multiple heterogeneous network segments, this network is unable to provide excellent quality of service (QoS). Based on the software-defined network and virtual network architecture, we abstract SAGOI-Net as a three-layer heterogeneous physical network resource, and propose a multi-domain virtual network embedding solution to optimize QoS. Specifically, before virtual network embedding, we collected SAGOI-Net's resource information through software-defined network and modeled it. In the virtual network embedding process, we first classify the virtual network request through K-means, and dynamically adjust the reward function to use reinforcement learning to solve the optimal virtual network embedding strategy. Finally, simulation experiments verify the effectiveness of the scheme.
引用
收藏
页码:1712 / 1723
页数:12
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