Quest Method for Organizing Cloud Processing of Airborne Laser Scanning Data

Vitalii Tkachov, Hunko Mykhailo
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引用次数: 2

Abstract

Nowadays, the processing of data obtained as a result of airborne laser scanning by a group of unmanned aerial vehicles includes up to eight different stages. From the point of view of organizing such processing, it is reasonable to use modern cloud technologies. The paper proposes a new method for processing this type of data based on the social model of completing game quests. Portions of data arrive at a computing system consisting of several clouds where, in turn, multifunctional SaaS nodes operate. Each of the nodes can process one data portion once, which allows to exclude the principle of exclusive use of one node for all stages of processing a data portion. The paper proposes an original algorithm for determining the optimal node for the next stage of processing, which takes into account: the time of data portions movement between SaaS nodes, in fact, processing and waiting in the node queue. Due to carried out experiments, it was found that the implementation of the developed quest method allows to reduce the workload of SaaS nodes by the uniform distribution of processing between all nodes, which was to an average of 30%. Additionally, in the case when the number of processing nodes is less than three, the efficiency of the method decreases to zero. This paper suggests that the ques method can also be used on board of the nodes of a flying sensor network during airborne laser scanning of the area in rapid-response systems.
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组织机载激光扫描数据云处理的任务方法
目前,一组无人机对机载激光扫描获得的数据的处理包括多达八个不同的阶段。从组织这种处理的角度来看,使用现代云技术是合理的。本文提出了一种基于游戏任务完成社交模型的数据处理新方法。部分数据到达由多个云组成的计算系统,而这些云又由多功能SaaS节点运行。每个节点可以一次处理一个数据部分,这就排除了在处理数据部分的所有阶段只使用一个节点的原则。本文提出了一种确定下一阶段处理的最优节点的原始算法,该算法考虑了数据部分在SaaS节点之间移动的时间,即节点队列中的处理和等待时间。经过实验发现,所开发的任务方法的实现可以通过在所有节点之间均匀分配处理来减少SaaS节点的工作量,平均为30%。此外,当处理节点数小于3时,该方法的效率降至零。本文认为,该方法也可用于快速响应系统中机载激光扫描区域时的机载传感器网络节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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