【摘 要】
：
在大规模分布式系统中,节点加入与退出所产生的扰动行为对系统性能有直接影响.当前关于节点扰动的行为模型主要描述节点达到和持续时长的统计特性,同时也对节点进行分类并赋
【机 构】
：
深圳大学计算机与软件学院,中国科学院沈阳计算技术研究所
论文部分内容阅读
在大规模分布式系统中,节点加入与退出所产生的扰动行为对系统性能有直接影响.当前关于节点扰动的行为模型主要描述节点达到和持续时长的统计特性,同时也对节点进行分类并赋予不同的统计值.从全球规模的视角上考察其扰动行为,需要在更高层面上加以描述.以各时区为单位划分其统计特性将合理解释网络规模呈周期性变化的现象,并可以隐含推测各时段网络的构成成份,进而可以用于优化覆盖网的维护或者备份数据的冗余管理等目的.时区扰动模型的基础在于全球节点的地理位置分布和各时区内用户相对统一的行为模式.在单个时区内用户可以进一步细分为不同类型,各个类型的用户群则可以用传统的到达间隔和会话持续时间来描述.根据此模型所产生出的扰动事件序列对系统节点扰动行为的模拟更加精确,解决了描述节点总数量变化的时间和快慢问题,同时也解决了这些数量变化是由哪些节点引起的问题.由于节点的变化更加精确并具有规律性,因此在对数据存储等应用上可以作为优化的依据之一.
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