计算机工程

• 移动互联与通信技术 • 上一篇    下一篇

实时以太网状态分析及其优化策略

金海波,仲崇权   

  1. (大连理工大学电子信息与电气工程学部,辽宁 大连 116024)
  • 收稿日期:2012-09-11 出版日期:2013-11-15 发布日期:2013-11-13
  • 作者简介:金海波(1983-),男,博士研究生,主研方向:无线传感器网络,现代有线通信技术;仲崇权,教授、博士
  • 基金项目:
    国家“863”计划基金资助项目(2007AA041407-04, 2007AA041301-6)

Real-time Ethernet State Analysis and Its Optimization Policy

JIN Hai-bo, ZHONG Chong-quan   

  1. (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China)
  • Received:2012-09-11 Online:2013-11-15 Published:2013-11-13

摘要:

为提高实时以太网通信性能和数据帧发送成功率,提出一种基于随机优化理论的实时以太网数据传输优化策略。对实时以太网传输状态进行分析,计算每种状态之间的转移概率,得到状态转移概率矩阵,通过求解平稳状态方程确定以太网处于每种状态的概率,并计算数据帧发生碰撞后每次重传成功概率,以数据帧发送成功率最大为目标函数,对节点发送速率进行优化。实验结果表明,与现有方法相比,该策略在网络传输成功率和吞吐量方面最多提高50.4%和23.4%,在端到端平均延时上也有所改善。

关键词: 实时以太网, 传输状态, 泊松过程, 半马尔科夫链, 平稳状态方程, 随机优化

Abstract: In order to improve the performance of real-time Ethernet and the transmission efficiency of data frames, an optimal policy for real-time Ethernet transmission is proposed based on stochastic optimization theory. The transmission states of real-time Ethernet are analyzed and transition probability matrix is derived by calculating the transition probabilities among any two different Ethernet states. Subsequently the probability of each Ethernet state is ascertained by solving steady-state equations and then the probabilities that frames retransmit successfully after collision occurred in each time are calculated. The aim of this paper is focused on optimizing the sending rate of nodes by maximizing the objective function of the successful transmission probability. Experimental results show that in the presented policy the maximum amount of improvement in terms of transmission successful rate and throughput of Ethernet is as high as 50.4% and 23.4% respectively in comparison with the existing method, and there is also certain improvement on average time-delay of point-to-point.

Key words: real-time Ethernet, transmission state, Poisson process, semi-Markov chain, steady state equation, stochastic optimization

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