Game Server Network Analysis Engine

游戏服务器网络分析引擎

基本信息

  • 批准号:
    489094-2015
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Engage Grants Program
  • 财政年份:
    2015
  • 资助国家:
    加拿大
  • 起止时间:
    2015-01-01 至 2016-12-31
  • 项目状态:
    已结题

项目摘要

Massively online games (MOG) often require players to be immersed in a real-time environment. Thus, the quality of service requirements implied in order to support MOG are some of the most challenging encountered by Internet applications. This can be particularly challenging to achieve, given the diversity in participant's physical location and shared nature of network resources. This project will investigate the utility of evolutionary computation for adaptively reconfiguring aspects of the client-server environment used to support MOG over widely dispersed end-users. MOG applications are characterized by data requirements that are comparatively light-weight, but very time sensitive. This is a distinct requirement from download based applications, such as Netflix, or communication style applications such as Skype. The objective of this project is to design and implement a system to map existing game servers and the network paths to those servers from the major Internet Exchanges or NAPs (Network Access Points). In order to achieve this, we will analyze the existing reconnaissance tools such as 'traceroute' and 'dig' with the objective of exploring their augmentation with network monitoring and congestion identification tools and techniques.
大型在线游戏(MOG)通常要求玩家沉浸在实时环境中。因此 为支持军事行动小组而提出的服务质量要求是最具挑战性的 通过互联网应用。考虑到参与者的多样性,这一点尤其具有挑战性。 物理位置和网络资源的共享性质。本项目将研究 用于自适应地重新配置客户机-服务器环境的各方面的进化计算 MOG覆盖广泛分散的最终用户。MOG应用程序的特点是数据需求, 重量相对较轻,但对时间非常敏感。这与基于下载的 应用,诸如Netflix,或通信风格应用,诸如Skype。本项目的目标 设计和实现一个系统,将现有的游戏服务器和网络路径映射到这些服务器, 主要的互联网交换或NAP(网络接入点)。为了实现这一点,我们将分析 现有的侦察工具,如“跟踪路由”和“挖掘”,目的是探索它们的增强 使用网络监控和拥塞识别工具和技术。

项目成果

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Heywood, Malcolm其他文献

Heywood, Malcolm的其他文献

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{{ truncateString('Heywood, Malcolm', 18)}}的其他基金

Scaling Genetic Programming to Complex Reinforcement Learning Tasks
将遗传编程扩展到复杂的强化学习任务
  • 批准号:
    RGPIN-2020-04438
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Scaling Genetic Programming to Complex Reinforcement Learning Tasks
将遗传编程扩展到复杂的强化学习任务
  • 批准号:
    RGPIN-2020-04438
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Scaling Genetic Programming to Complex Reinforcement Learning Tasks
将遗传编程扩展到复杂的强化学习任务
  • 批准号:
    RGPIN-2020-04438
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Permutation based task transfer for genetic programming
基于排列的遗传编程任务转移
  • 批准号:
    RGPIN-2015-06117
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Coevolutionary automatic game content generation of physics and flighting style games
物理和飞行风格游戏的协同进化自动游戏内容生成
  • 批准号:
    499792-2016
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Permutation based task transfer for genetic programming
基于排列的遗传编程任务转移
  • 批准号:
    RGPIN-2015-06117
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Permutation based task transfer for genetic programming
基于排列的遗传编程任务转移
  • 批准号:
    RGPIN-2015-06117
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Coevolutionary automatic game content generation of physics and flighting style games
物理和飞行风格游戏的协同进化自动游戏内容生成
  • 批准号:
    499792-2016
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Permutation based task transfer for genetic programming
基于排列的遗传编程任务转移
  • 批准号:
    RGPIN-2015-06117
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Coevolutionary automatic game content generation of physics and flighting style games
物理和飞行风格游戏的协同进化自动游戏内容生成
  • 批准号:
    499792-2016
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants

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