Ameba Network Architecture based on Advanced Multi-Layer Network and Its Configuration Algorithm

被引:0
|
作者
Tode, Hideki [1 ]
Tada, Kenji [1 ]
Kohama, Shuta [1 ]
机构
[1] Osaka Prefecture Univ, Dept Comp Sci & Intelligent Syst, Naka Ku, Sakai, Osaka 5998531, Japan
关键词
OPTICAL NETWORKS;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In future networks, unknown and unintended traffic implosion and hot spot congestion may give serious damage because of heavy traffic implosion and rapid traffic fluctuation. To tackle this issue, the research on virtual network and next-generation network has been advancing. As a result, network control can be much more flexible than before. However, from the perspective of network architecture, the current approaches with fixed IP node/OXC node location still remain larger possibility to enhance the flexibility. Namely, novel solution is eagerly anticipated. For realization of highly advanced next-generation network, this paper suggests novel network architecture that can decrease a network scale, especially, not at the link level but at the entire topology level by adaptively leveraging the underlying OXC networks and by making virtual node function composed of several distantly positioned OXCs, named "Ameba Node". Main benefits of the proposed Ameba Network include as follows. First, network-level load balancing can be attained by adaptively re-configuring the Ameba network topology without any other complicated traffic engineering. Second, simple and naive routing algorithm and network controls would be applied thanks to its network level load-balancing capability. Thirdly, IP routing /optical-layer routing processes, and the resultant network resources can be balanced flexibly. Finally, flexibility on node renewal or network scale-up can be realized by not replacing old routers to most advanced brand-new ones but reconfiguring the shape of Ameba nodes in network adaptively. We also evaluate its effectiveness by computer simulation.
引用
收藏
页码:3481 / 3486
页数:6
相关论文
共 50 条
  • [31] A buffered online transfer learning algorithm with multi-layer network
    Kang, Zhongfeng
    Yang, Bo
    Nielsen, Mads
    Deng, Lihui
    Yang, Shantian
    NEUROCOMPUTING, 2022, 488 : 581 - 597
  • [32] Multi-layer resource scheduling architecture and algorithm for a service-oriented optical network based on a fine grain OTN
    Zhao, Yang
    Lin, Yi
    Li, Yunbo
    Zhang, Dechao
    Liu, Yucong
    Zheng, Yu
    Wang, Dong
    Liu, Sheng
    Cao, Shan
    Feng, Haoyu
    Li, Han
    Liu, Xiang
    JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, 2024, 16 (10) : F13 - F25
  • [33] A Multi-objective Evolutionary Algorithm Based on Multi-layer Network Reduction for Community Detection
    Qi, Xin
    He, Langzhou
    Wang, Jiaxin
    Du, Zhanwei
    Luo, Zheng
    Li, Xianghua
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2022, PT III, 2022, 13370 : 141 - 152
  • [34] Implementation of multi-layer neural network system for neuromorphic hardware architecture
    Sun, Wookyung
    Park, Junhee
    Jo, Sumin
    Lee, Jungwon
    Shin, Hyungsoon
    2019 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC), 2019, : 312 - 313
  • [35] Design of a Hybrid Multi-layer Satellite Backbone Network Architecture br
    Li, Wenping
    Bai, Hefeng
    Yi, Zhao
    Feng, Xuzhe
    Shao, Fujie
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2023, 45 (02) : 472 - 479
  • [36] Network services deployment for QoS provisioning in a multi-layer diffServ architecture
    Nikolouzou, E.
    Maniatis, S.
    Sampatakos, P.
    Tsetsekas, H.
    Venieris, I.
    Advances in Automation, Multimedia and Video Systems, and Modern Computer Science, 2001, : 200 - 205
  • [37] A study on multi-layer service network architecture in IP optical networks
    Kojima, H
    Takeda, T
    Inoue, I
    APCC 2003: 9TH ASIA-PACIFIC CONFERENCE ON COMMUNICATION, VOLS 1-3, PROCEEDINGS, 2003, : 458 - 462
  • [38] Network Security Based on GCN and Multi-Layer Perception
    Yu, Wei
    Liu, Huitong
    Song, Yu
    Wang, Jiaming
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2025, 16 (01) : 471 - 480
  • [39] Image classification algorithm based on deep neural network and multi-layer feature learning
    Huang, Yiying
    Wang, Junrong
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 32 - 33
  • [40] Image Classification Algorithm Based on Deep Neural Network and Multi-Layer Feature Learning
    Guo, Guangxing
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 124 : 287 - 287