Network engineering 2000

D.J.Y. Lee, WuSeong Lee
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引用次数: 1

Abstract

Introduction of location technology shifts the paradigm of network engineering. It opens the door for providing a revolutionary approach for network engineering. The algorithm discussed in this paper provides a way of combining parameters made available through location technology and currently available system parameters for future network engineering and operation. The "continuous" snapshots of the network can be parsed and intelligently stored into the network performance data warehouse as images. These images can be analyzed off-line to select certain "troubled" instances of the network (this can be site specific). These instances can, then, be analyzed through simulation and/or engineer intervention. Optimized solution can be developed. These "troubled" instance and associated solutions can be fed back into the "virtual network engineer" simulated by the AI engine to increase its knowledge base. Later, the intelligent pseudo "network engineer" can identify "troubled" network instance and compare with the existing images and associated solution in the data warehouse. The existing solution then can be applied to perform real time automated network engineering. This is done through image processing by selecting the most similar network instance and associated solution. By saving more and more "troubled" instances and developed associated solutions, the "virtual network engineer" increases its knowledge bases and becomes more and more "intelligent" and can handle future "troubled" instance based on the "experience". For example, the dynamic power allocation and individual specific dedicated dynamic SHO thresholds for each mobile can be combined to efficiently engineer the network performance once the mobile location and associated network characteristics can be identified.
网络工程2000
定位技术的引入改变了网络工程的范式。它为提供一种革命性的网络工程方法打开了大门。本文所讨论的算法为今后的网络工程和运行提供了一种将定位技术获得的参数与现有系统参数相结合的方法。网络的“连续”快照可以被解析,并作为图像智能地存储到网络性能数据仓库中。这些图像可以离线分析,以选择网络的某些“问题”实例(这可以是特定于站点的)。然后,可以通过模拟和/或工程师干预来分析这些实例。可以开发出优化的解决方案。这些“麻烦”实例和相关解决方案可以反馈给人工智能引擎模拟的“虚拟网络工程师”,以增加其知识库。随后,智能伪“网络工程师”可以识别出“问题”网络实例,并与数据仓库中的现有图像和相关解决方案进行比较。然后将现有的解决方案应用于实时自动化网络工程。这是通过选择最相似的网络实例和相关解决方案进行图像处理来完成的。“虚拟网络工程师”通过保存越来越多的“麻烦”实例和开发相关的解决方案,增加了知识库,变得越来越“智能”,可以根据“经验”处理未来的“麻烦”实例。例如,一旦确定了移动位置和相关网络特征,就可以将每个移动设备的动态功率分配和个别特定的专用动态SHO阈值结合起来,有效地设计网络性能。
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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