面向高性能响应性分布式gis (D-GIS)的动态分布式负载均衡

Aissatou Diasse, F. Kone
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引用次数: 3

摘要

近年来,一些研究成果被用于提高分布式GIS的性能。虽然更多的工作集中在动态地图上的平铺图和简单的符号上,但GIS社区到目前为止还没有解决负载平衡GIS服务器的问题。因此,本文提出了一种动态分布式的D-GIS负载平衡方法,通过一组GIS服务器来处理客户端的请求,从而快速地将信息呈现给客户端界面。在该模型中,引入并定义了几个概念:物理机内的虚拟服务器构成了单个GIS服务器的设置环境;负载哈希表包含了虚拟服务器的容量、实时负载和其他必需元素的信息;请求分割表根据输入区域的信息量分割请求并存储请求任务组成以供以后重构。最后,我们有分布式故障转移回调函数表第一级(分别为第二级),它确定请求是否已被所选的虚拟服务器(分别为物理机)成功处理。该表允许将相同的请求发送回另一个虚拟服务器(分别为物理节点)。在失败的情况下定义了两个负载处理程序(主和辅助)。我们的模型通过提供高效的节点选择来实现高效的负载均衡;优化请求路由;管理节点故障转移;涉及客户端请求划分,引入方法类型分解。仿真结果表明,该算法在执行GIS操作时具有较低的响应时间。
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Dynamic-Distributed Load Balancing for Highly-Performance and Responsiveness Distributed-GIS (D-GIS)
In recent years, several results have been introduced to enhance distributed GIS performance. While much more efforts have focused on tile map and simple symbologies on dynamic map, load balancing GIS servers have not been addressed by the GIS community so far. This paper, therefore, proposed dynamic distributed load balancing for D-GIS in order to quickly render information to client interface by involving a set of GIS servers which process clients’ requests depending of an algorithm. In the model, several concepts were introduced and defined: Virtual Server within physical machine which constitutes a setup environment for a single GIS server, Load Hash Table which contains information about virtual server’s capacity, real-time load and other mandatory elements, Request Split Table which splits requests depending of the input area’s Quantity of Information and stores request tasks composition for later reconstitution. At last we have Distributed Failover Callback Function Table level one (respectively level two) which determines whether or not the request had been successfully processed by the chosen virtual server (respectively physical machine). This table allows sending back the same request to another virtual server (respectively physical node). Two load handlers (primary and secondary) are defined in case of failure. Our Model achieves efficient load balancing by: providing efficient node selection; optimizing request routing; managing node failover; involving client’s request partitioning and introducing method type decomposition. A simulation of the algorithm shows a low response time when performing GIS operations.
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