• DocumentCode
    485269
  • Title

    A new knowledge-based partitioning algorithm in CVE systems

  • Author

    Hu, X.M. ; Zhu, W.H. ; yu, tao

  • Author_Institution
    CIMS & Robot Cent, Shanghai Univ., Shanghai
  • fYear
    2007
  • fDate
    12-14 Dec. 2007
  • Firstpage
    225
  • Lastpage
    228
  • Abstract
    Collaborative virtual environment (CVE) system supports large number of users to explore a virtual world and interact with each other through networks, so one of the key issues in the design of scalable CVE systems is the partitioning problem. Existing partitioning algorithms in CVE systems based on multiple-server architecture, in our opinion, hardly consider reducing inter-server messages. In this paper, we propose a new knowledge-based partitioning algorithm which can effectively decrease inter-server messages in CVE systems. In the new knowledge-based partitioning algorithm, using the knowledge matrix built on the communication character of the virtual environment, the cells that have high communication cost are chosen as the centers of regions while the cells that have low communication cost are allocated for the edge of the regions. The experimental results, given in Fig. 6 and Table 2 in the full paper, show preliminarily that the knowledge-based partitioning algorithm does decrease the traffic among the servers in the system and improve the partitioning performance.
  • Keywords
    knowledge based systems; matrix algebra; virtual reality; CVE systems; collaborative virtual environment system; inter-server messages; knowledge matrix; knowledge-based partitioning algorithm; multiple-server architecture; scalable CVE systems; virtual world; Area of Interest (AOI); Collaborative Virtual Environment (CVE); Partitioning;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Wireless, Mobile and Sensor Networks, 2007. (CCWMSN07). IET Conference on
  • Conference_Location
    Shanghai
  • ISSN
    0537-9989
  • Print_ISBN
    978-0-86341-836-5
  • Type

    conf

  • Filename
    4786178