Computational intelligence in management of ATM networks: a survey of the current state of research
Computational intelligence in management of ATM networks: a survey of the current state of research
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ATM 网络管理中的计算智能：研究现状调查
DOI：
10.1117/12.367704
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
A. Vasilakos
中科院分区：
文献类型：
--
作者：
Y. Sekercioglu;A. Pitsillides;A. Vasilakos
众所周知，由于网络结构的复杂性、所支持的服务的性质以及所涉及的各种动态参数，为异步传输模式（ATM）网络设计有效的控制策略是困难的。此外，不确定性所涉及的网络参数的识别导致ATM网络的分析建模几乎是不可能的。这使得经典的控制系统设计方法（依赖于这些模型的可用性）的应用程序的问题更加困难。因此，一些研究人员正在寻找替代的非分析控制系统的设计和建模技术，有能力科普这些困难，设计有效的，强大的ATM网络管理计划。这些方案采用了人工神经网络、模糊系统和基于进化计算的设计方法。在这项调查中，ATM网络管理研究的现状，采用这些技术的技术文献报道进行了总结。所采用的方法的显着特点进行审查。
Designing effective control strategies for Asynchronous Transfer Mode (ATM) networks is known to be difficult because of the complexity of the structure of networks, nature of the services supported, and variety of dynamic parameters involved. Additionally, the uncertainties involved identification of the network parameters cause analytical modeling of ATM networks to be almost impossible. This renders the application of classical control system design methods (which rely on the availability of these models) to the problem even harder. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling techniques that have the ability to cope with these difficulties to devise effective, robust ATM network management schemes. Those schemes employ artificial neural networks, fuzzy systems and design methods based on evolutionary computation. In this survey, the current state of ATM network management research employing these techniques as reported in the technical literature is summarized. The salient features of the methods employed are reviewed.