CAREER: "Analysis and Control of Network Delay"

职业：“网络延迟分析与控制”

• 批准号: 0747525
• 负责人: Michael Neely
• 金额: $ 40万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747525&HistoricalAwards=false
• 关键词: CAREER; Analysis; Control; Network; Delay

CAREER Project: Analysis and Control of Network DelayMichael J. NeelyUniversity of Southern Californiahttp://www-rcf.usc.edu/~mjneely/Abstract: This research investigates communication strategies for time-varying networks, with the goal of developing a fundamental theory for analysis and control of network delay. While much is known about network throughput optimization, the delay problem is more complex and less understood. Delay-aware algorithms must be designed for very large systems and must be able to optimally adapt to environmentswhere future network traffic, link conditions, and mobile user locations are uncertain. This project seeks to establish mathematical techniques for computing tight delay bounds, and to develop scheduling and routing algorithms that achieve provably high throughput with order-optimal delay. This project also explores intelligent mechanisms for scheduling redundant packet information and for making use of (and controlling) network mobility. Indeed, although redundancy and mobility increase the complexity of decision options, both can be used advantageously to improve network performance.This project also introduces a new topic of mathematical optimization: The study of controlling Lagrange multipliers. Specifically, it is known that network optimization is closely related to convex programming theory, where Lagrange multipliers can play a role analogous to queue backlogs and/or "network prices." However, there has been little research in the area of controlling the magnitude of the Lagrange multipliers that are used in an optimization problem. A general theory of controlled Lagrange multipliers is thus important to this study of network delay and for related problems of network pricing and economics. Such a theory may also prove useful in other optimization contexts, and may have broader applications to machine learning and online dynamic programming.

职业项目：对网络延迟Michael J. Neelyuniversity的分析和控制。 虽然对网络吞吐量优化知之甚少，但延迟问题更为复杂且不了解。 延迟感知算法必须针对非常大的系统设计，并且必须能够最佳地适应未来的网络流量，链接条件和移动用户位置。 该项目旨在建立用于计算紧密延迟界限的数学技术，并开发计划和路由算法，这些算法可通过订单 - 最佳延迟实现可证明高通量的吞吐量。 该项目还探讨了用于调度冗余数据包信息并利用（和控制）网络移动性的智能机制。 确实，尽管冗余和流动性会增加决策选择的复杂性，但两者都可以有利地提高网络性能。该项目还引入了数学优化的新主题：控制拉格朗日乘数的研究。 具体而言，众所周知，网络优化与凸编程理论密切相关，在该理论中，Lagrange乘数可以发挥类似于排队积压和/或“网络价格”的作用。但是，在控制优化问题中使用的Lagrange乘数的大小的领域很少进行研究。 因此，受控的Lagrange乘数的一般理论对于这项网络延迟研究以及网络定价和经济学的相关问题至关重要。 这种理论也可能在其他优化环境中有用，并且在机器学习和在线动态编程中可能更广泛地应用。