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    Adaptive Client to Mirrored-Server Assignment for Massively Multiplayer Online Games

    20748.pdf (508.7Kb)
    Access Status
    Open access
    Authors
    Webb, Steven
    Soh, Sieteng
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Webb, Steven Daniel and Soh, Sieteng. 2008. : Adaptive Client to Mirrored-Server Assignment for Massively Multiplayer Online Games, in Rajaie, Reza and Zimmermann, Roger (ed), Fifteenth Annual Multimedia Computing and Networking, 30-31 January 2008, pp. 68180I-1. San Jose, California, USA: SPIE and IS&T.
    Source Conference
    Fifteenth Annual Multimedia Computing and Networking
    DOI
    10.1117/12.775145
    Faculty
    Faculty of Engineering and Computing
    Division of Engineering, Science and Computing
    Department of Computing
    School
    Computing
    URI
    http://hdl.handle.net/20.500.11937/43965
    Collection
    • Curtin Research Publications
    Abstract

    The Mirrored Server (MS) architecture for network games uses multiple mirrored servers across multiple locations to alleviate the bandwidth bottleneck and to reduce the client-to-server delay time. Response time in MS can be reduced by optimally assigning clients to their mirrors. The goal of optimal client-to-mirror-assignment (CMA) is to achieve the minimum average client-to-mirror delay considering player joins (CMA-J) and leaves (CMA-L), and mirrors with limited capacity. The existing heuristic solution considers only CMA-J, and thus the average delay of the remaining players may increase when one or more players leave. Furthermore, the solution ignores mirror capacity, which may overload mirrors. In this paper we present a resource usage model for the MS architecture, and formally state the CMA problem. For both CMA-J and CMA-L we propose a polynomial time optimal solution and a faster heuristic algorithm that obtains near optimal CMA. Our simulations on randomly generated MS topologies show that our algorithms significantly reduce the average delay of the existing solution. We also compare the merits of the solutions in terms of their optimality and running time efficiency.

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