• DocumentCode
    3492447
  • Title

    Bio-inspired balanced tree structure dynamic network

  • Author

    Liu, Fengchen ; Ding, Yongsheng ; Gao, WeiXun

  • Author_Institution
    Coll. of Inf. Sci. & Technol., Donghua Univ., Shanghai, China
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    222
  • Lastpage
    229
  • Abstract
    Bio-networks have the natural advantages of autonomy, scalability, and adaptability which are challenges for computer networks, especially P2P networks. We present a bio-inspired dynamic balanced tree structure network (called bio-block) based dynamic network. Every bio-block is a unique bio-entities collection with emergent service. This network has two parts, non-Service part (bio-entity is unit node) and in-Service part (bio-block is unit node). Useful bio-entities are dynamically transferring between these two part to keep the balance, and improve resources usage. This network inherits the balanced structure and O(nlogN) search steps with total N resources and n resources service request. It also eliminates redundancies by taking advantage of strong adaptability of bio-network which are composed of bio-entities. Any node in this balanced tree structured network can join and leave dynamically. Intensive experimental results show that the state of this network is converged when service distribution is stable. Moreover, theoretical results support an efficient search operation.
  • Keywords
    bio-inspired materials; computational complexity; network theory (graphs); peer-to-peer computing; resource allocation; search problems; trees (mathematics); P2P network; balanced tree structured network; bioinspired balanced tree structure dynamic network; computer network; emergent service; in-service part; nonservice part; service distribution; unique bioentities collection; Biological system modeling; Heuristic algorithms; Immune system; Redundancy; Routing; Silicon;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033225
  • Filename
    6033225