Application of Time-Varying Graph Theory over the Space Information Networks

被引:41
|
作者
Zhang, Tao [1 ]
Li, Jiandong [2 ]
Li, Hongyan [3 ]
Zhang, Shun [3 ]
Wang, Peng [3 ]
Shen, Haiying [4 ]
机构
[1] Xidian Univ, Inst Informat & Sci, State Key Lab Integrated Serv Networks, Xian, Peoples R China
[2] Xidian Univ, Xian, Peoples R China
[3] Xidian Univ, State Key Lab Integrated Serv Networks, Xian, Peoples R China
[4] Univ Virginia, Dept Comp Sci, Charlottesville, VA 22903 USA
来源
IEEE NETWORK | 2020年 / 34卷 / 02期
基金
中国国家自然科学基金;
关键词
Routing; Dynamic scheduling; Satellites; Network slicing; Quality of service; Orbits; VIRTUALIZATION;
D O I
10.1109/MNET.001.1900245
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
SIN is constructed to support real-time data acquisition, massive data transmission and processing, and systematized information services. Different from static networks, both the network topologies and the available network resources are dynamic in SIN. However, the existing networking technologies treat SIN as segmented static networks, and ignore the relationship among different segmented subnetworks, which results in low resource utilization and poor transmission QoS. This article exploits the time-varying graph theory to characterize and allocate the dynamic resources of SIN. We first introduce the overall SIN architecture, and discuss its key technologies in terms of network slicing and dynamic routing. Then, we construct a novel STAG to precisely depict the multi-dimensional time-varying resources and reveal their connection relationships in SIN. With STAG, we propose the dynamic network slicing strategy to build the dedicated network slices for different services, and design the intra-slice routing scheme to guarantee the service transmission QoS. Simulation results demonstrate our methods can complete more space missions with higher resource utilization. Finally, we discuss the promising future of the time-varying graph theory.
引用
收藏
页码:179 / 185
页数:7
相关论文
共 50 条
  • [11] Modeling information diffusion in time-varying community networks
    Cui, Xuelian
    Zhao, Narisa
    CHAOS, 2017, 27 (12)
  • [12] An Information-Based Theory of Time-Varying Liquidity
    Daley, Brendan
    Green, Brett
    JOURNAL OF FINANCE, 2016, 71 (02): : 809 - 870
  • [13] DISTRIBUTED NONCONVEX OPTIMIZATION OVER TIME-VARYING NETWORKS
    Di Lorenzo, Paolo
    Scutari, Gesualdo
    2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 4124 - 4128
  • [14] Renormalization group theory for percolation in time-varying networks
    Karschau, Jens
    Zimmerling, Marco
    Friedrich, Benjamin M.
    SCIENTIFIC REPORTS, 2018, 8
  • [15] Renormalization group theory for percolation in time-varying networks
    Jens Karschau
    Marco Zimmerling
    Benjamin M. Friedrich
    Scientific Reports, 8
  • [16] A Finite-Time Protocol for Distributed Time-Varying Optimization Over a Graph
    Santilli, Matteo
    Furchi, Antonio
    Oliva, Gabriele
    Gasparri, Andrea
    IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2024, 11 (01): : 53 - 64
  • [17] Time-Varying Resource Graph Based Processing on the Way for Space-Terrestrial Integrated Vehicle Networks
    Chen, Long
    Tang, Feilong
    Liu, Jiacheng
    Li, Xu
    Zhu, Yanmin
    Yu, Jiadi
    Yang, Laurence T.
    Li, Zhetao
    Yao, Bin
    Yu, Yichuan
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (02) : 1985 - 2002
  • [18] Time deterministic routing algorithm and protocol based on time-varying graph over the space-ground integrated network
    Li H.
    Zhang T.
    Zhang J.
    Shi K.
    Zeng P.
    Tongxin Xuebao/Journal on Communications, 2020, 41 (10): : 116 - 129
  • [19] FUZZY DESCRIPTORS OF TIME-VARYING DATA - THEORY AND APPLICATION
    BRIDGES, SM
    HIGGINBOTHAM, C
    MCKINION, JM
    HODGES, JE
    AI APPLICATIONS, 1995, 9 (02): : 1 - 14
  • [20] DISTRIBUTED SADDLE-POINT OPTIMIZATION OVER TIME-VARYING NETWORKS WITH PROBABILISTICALLY QUANTIZED INFORMATION
    Zhou, Huiqin
    Yuan, Deming
    Wang, Baoyun
    2014 12TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP), 2014, : 2216 - 2220