电信科学 ›› 2012, Vol. 28 ›› Issue (8): 105-112.doi: 10.3969/j.issn.1000-0801.2012.08.019

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网络柔性重构的智能机理浅析

兰巨龙,程东年,王雨,张风雨   

  1. 国家数字交换系统工程技术研究中心 郑州450002
  • 出版日期:2012-08-15 发布日期:2017-06-14
  • 基金资助:
    国家重点基础研究发展计划(“973”计划)基金资助项目;国家重点基础研究发展计划(“973”计划)基金资助项目

Initial Analysis on Intelligence Mechanisms of Reconfigurable Network Abstract Flexibility,with which a reconfigurable network adaptively tunes its structure,plays a critical role in accommodating varieties of applications with desirable service levels.Most studies avalaible,however,rarely provide clear answers about the mechanism of flexibility that supports network reconfigurations.For the mechanism more clear,this paper clarifies a key target of the consistant matching of service outcomes to application demands.What is then revealed includes the kernal characteristic,so-called outcome following demand with gardually tuning,of the reconfiguration flexibility.Furthermore,four inherent schemes and frameworks representing reconfiguration flexibility are respectively examined in depth.They are the knowledeg-driven architecture of network functionalities,cross-layer interactions of functionalities,distributed and collaborative sensing,and distributed intelligece.Finally,the method of ant conoly optimization(ACO)is emphasized by explaining its effectiveness in optimizing non-linear problems such as performence-constrained routing at small time scales.Specifically,both the advantage and significance of emergence,the prominent feature of ACO,are illustrated.

Julong Lan,Dongnian Cheng,Yu Wang,Fengyu Zhang   

  1. National Digital Switching System Engineering &Technological R&D Center,Zhengzhou 450002,China
  • Online:2012-08-15 Published:2017-06-14

摘要:

作为可重构网络结构调整的方式,柔性对于服务效果与应用要求“一致匹配”至关重要,但当前的研究并未清晰地回答支持网络柔性重构的内在工作机理。在说明重构方式应服务于效果与要求“一致匹配”这一核心目标的基础上,本文揭示了柔性重构的渐变跟随特征,分别从知识驱动和功能结构、网络功能的跨层交互、合作感知和分布式智能4个方面,具体阐述了支持结构柔性的内在工作机理和结构,针对受性能约束的路由这类小尺度非线性优化问题,强调了采用蚁群优化这种以“涌现”形式表达最优解方法的优势和意义。

关键词: 可重构网络, 分布式计算, 人工智能, 蚁群优化

Abstract:

Key words: reconfigurable network, distributed computing, artificial intelligence, ant colony optimization

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