Inference and Performance Problems Related to High Variability Phenomena in Measured Data Network Traffic

与测量的数据网络流量中的高变异性现象相关的推理和性能问题

• 批准号: 9818076
• 负责人: Sidney Resnick
• 金额: $ 4.28万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-04-15 至 1999-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9818076&HistoricalAwards=false
• 关键词: Inference; Performance; Problems; Related; Variability

A cooperative arrangement between Cornell University and AT&T Labs-Research allows Cornell's Professor Sidney Resnick to visit AT&T Labs-Research to collaborate with Dr. Walter Willinger and others at AT&T Labs-Research on problems related to (i) providing an in-depth understanding of the dynamic nature of today's data network traffic and (ii) exploiting the newly-gained insights for the economic design and effective and efficient management of modern high-speed communications networks. The work focuses on the dual themes of how to construct and fit models that can account for empirically observed data network traffic characteristics such as high-variability, heavy-tails, long-range dependence, self-similar behavior. Phenomena exhibiting heavy tails and/or long-range dependence, although departing in marked fashion from classical assumptions of Gaussian distributions, finite variances and short-range interactions, have been frequently noted in a broad array of fields such as insurance, economics and finance. These phenomenahave recently attracted renewed interest due to their ubiquitous presence in traffic measurements from today's data networks and especially because they often play havoc with established network and traffic engineering methodologies that are based on conventional (i.e., telephony-based) wisdom. As a result, this networking application opens up a variety of new and fundamental research topics at the intersection of applied probability and statistics and provides unique opportunities for close collaborations between mathematicians and engineers to work on technically challenging problems that are, at the same time, highly relevant in practice. This GOALI project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).

康奈尔大学和AT&amp；T Labs-Research之间的合作安排允许康奈尔大学的Sidney Resnick教授访问AT&amp；T Labs-Research，与AT&amp；T Labs的Walter Willinger博士和其他人合作研究与以下相关的问题：(I)提供对当今数据网络流量动态性质的深入了解，以及(Ii)利用新获得的对现代高速通信网络的经济设计和有效和高效管理的见解。这项工作集中在如何构建和拟合能够解释经验观察到的数据网络流量特征的双重主题，如高变异性、重尾、长范围相关性、自相似行为。重尾和/或长程相关性的现象，尽管明显背离了高斯分布、有限方差和短程相互作用的经典假设，但在保险、经济和金融等广泛领域经常被注意到。这些现象最近重新引起了人们的兴趣，因为它们在当今数据网络的流量测量中无处不在，特别是因为它们经常对基于传统(即基于电话)的智慧的已建立的网络和流量工程方法造成严重破坏。因此，这一联网应用程序在应用概率和统计学的交叉点上开辟了各种新的和基本的研究课题，并为数学家和工程师之间的密切合作提供了独特的机会，以解决同时在实践中高度相关的具有技术挑战性的问题。该GOALI项目由MPS多学科活动办公室(OMA)和数学科学处(DMS)共同支持。