Postdoc: Teletraffic Modelling and Analysis of Communications Networks with Parallelizable Algorithms and Their Implementation

博士后：具有可并行算法的通信网络的远程流量建模和分析及其实现

• 批准号: 9625748
• 负责人: Khosrow Sohraby
• 金额: $ 4.62万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 2000-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9625748&HistoricalAwards=false
• 关键词: Postdoc; Teletraffic; Modelling; Analysis; Communications

Design, dimensioning and control of high-speed telecommunication networks requires a thorough understanding of traffic characteristics of various types of communication services and the ability to use these characteristics for performance assessment. Teletraffic modeling techniques existing in the literature either lead to very large-scale structured Markov chain solution problems for which available algorithmic approaches suffer from huge CPU time requirements, or to models for which, at this time, there are no pratically analytical results for queuing analysis. The PIs do teletraffic modeling consisting of simple, general and systematic algorithms to model building based on D-BMAP's (discrete-time Batch Markovian Arrival Process). The modeling process is desired to capture the first and second order statistics of the original traffic streams that are known to play the dominant role in teletraffic performance. Moreover, the PIs propose parallel algorithms for modeling as an alternative to techniques based on serial procedures for large-scale nonlinear optimization problems. Such traffic models when fed into single server queues can be analyzed by the paradigm of M/G/1 or G/M/1 type Markov chains. They propose to use matrix sign function based algorithms for the associated invariant subspace computation . These algorithms have high convergence rates (unlike the linear convergence rates of existing algorithms), they are amenable to parallelization and, therefore, it is now possible to efficiently analyze moderately sized M/G/1 and G/M/1 type Markov chains.

高速电信网络的设计、规模确定和控制需要全面了解各种类型通信服务的流量特征以及利用这些特征进行性能评估的能力。 文献中存在的电信流量建模技术要么导致非常大规模的结构化马尔可夫链解决问题，其中可用的算法方法受到巨大的CPU时间要求的影响，要么导致目前没有用于排队分析的实际分析结果的模型。 PI 进行电信流量建模，包括简单、通用和系统的算法，以基于 D-BMAP（离散时间批量马尔可夫到达过程）进行模型构建。 建模过程需要捕获已知在电信业务性能中起主导作用的原始业务流的一阶和二阶统计数据。此外，PI 还提出了用于建模的并行算法，作为针对大规模非线性优化问题的基于串行过程的技术的替代方案。 当输入到单个服务器队列中时，这种流量模型可以通过 M/G/1 或 G/M/1 类型马尔可夫链的范式进行分析。 他们建议使用基于矩阵符号函数的算法来进行相关的不变子空间计算。 这些算法具有高收敛速度（与现有算法的线性收敛速度不同），它们适合并行化，因此，现在可以有效地分析中等大小的 M/G/1 和 G/M/1 类型马尔可夫链。