Content Grid Load Balancing Algorithm for Large-Scale Vector Data in the Server Cluster Concurrent Environment
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摘要: 提出了一种基于对自组织映射聚类的道路网网格模式识别方法。以道路网中的网眼为基本单元,从网眼自身形状特征、相邻网眼的形状特征以及与周围网眼的关系等方面定义了5个参量。将由5个参量描述的网眼及由CRITIC方法导出的参量权重作为自组织映射的输入,经过训练,运用犓means方法对神经元码书向量进行聚类。对深圳市道路网数据进行了实验和对比分析,结果表明该方法能有效识别网格模式。Abstract: In order to improve the concurrent access performance with large-scale vector data in WebGIS,a content grid load balancing algorithm is proposed.The server processing capability,service contents,and request time are taken into account.A proposed method to divide large-scale vector data into a content grid,the algorithm of content automatic identification,analysis,aggregation and feedback in the server cluster concurrent environment is discussed.This algorithm implements all servers in the cluster to complete visualization tasks submitted by clients at the same time and realizes task-oriented load balancing.For the extraction and display of large-scale and high-intensity vector data,the algorithm balances the servers’ load efficiently and responds to requests in minimal time.As compared to t traditional load balancing algorithms,the algorithm proposed in this paper has the best performance.The larger the scale,the more obvious is the load balancing effect.
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