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

康奈尔大学和AT T实验室研究所之间的合作安排允许康奈尔大学的教授西德尼·雷斯尼克访问AT T实验室研究所，与沃尔特·威林格博士和AT T实验室研究所的其他人合作，研究与以下有关的问题：（i）深入了解当今数据网络流量的动态性质;（ii）利用新获得的见解进行经济设计和现代高速通信网络的有效和高效管理。 这项工作的重点是如何构建和拟合模型，可以解释经验观察到的数据网络流量特性，如高变异性，重尾，长程依赖，自相似行为的双重主题。 表现出重尾和/或长程相关性的现象，尽管明显偏离了高斯分布、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中经常被注意到。 这些现象最近吸引了新的兴趣，这是由于它们在来自当今数据网络的流量测量中的普遍存在，特别是因为它们经常对基于传统（即，基于电话的）智慧。因此，这种网络应用在应用概率和统计的交叉点上开辟了各种新的基础研究课题，并为数学家和工程师之间的密切合作提供了独特的机会，以解决技术上具有挑战性的问题，同时，在实践中高度相关。 该GOALI项目由MPS多学科活动办公室（OMA）和数学科学部（DMS）联合支持。

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).