Copula-based Dependence Modeling for Computer and Communication Networks

计算机和通信网络基于 Copula 的依赖建模

• 批准号: RGPIN-2018-03896
• 负责人: Wu, Kui
• 金额: $ 3.5万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712673
• 关键词: Copula; based; Dependence; Modeling; Computer

With the fast advance of network technologies, the Internet has become an indispensable part of modern society. In less than 50 years, the Internet has become not only the major platform for entertainment, communication, and social networking, but also the main driving force for many new business services. Under the hood of diverse applications over the Internet are core mathematical models and algorithms that support their smooth operation. Improving the core models and algorithms will create a significant impact on the whole ecosystem of the Internet. We are thus motivated to investigate fundamental models that are critical to the future success of many internet applications. Looking back at history, we find that various dependence models have been proposed in every stage of research and development in the Internet. In theory, Internet traffic dependence models, together with queueing theory, lay the foundation for theoretical performance analysis of modern computer networks. In practice, dependence models are the basis for traffic engineering, network capacity planning, and prediction-based resource provisioning. Overall, the Internet essentially relies on various dependence models. While extensive work has been done on dependence modeling for computer and communication networks, the rich dependence structures in network traffic, particularly those beyond the scope of linear dependence, have not been fully explored. The goal of this NSERC discovery proposal is to push the state-of-the-art by exploring the power of copulas. Copulas are dependence models that are able to capture the functional dependence between/within network traffic flows and have shown promising results in traffic prediction, automatic resource adjustment, and network sampling. With mathematical analysis, simulation, and real-world experiments, the project will address two interweaving research themes: copula-based network performance tomography, and copula models for interdependent networks. In the first theme, we will develop a reliable network tomography method which can infer the network performance based on observations from a small number of monitoring points. In the second theme, we will study the system of interdependent networks, where the performance of one network depends on the performance of other networks. The research will enrich the toolset of dependence modeling for computer and communication networks and will benefit many real-world applications, including for example real-time multimedia delivery, performance monitoring for large-scale online broadcasting, and smart grid communication. By applying the theoretical results into our existing projects with industrial partners in Canada, our research results can benefit Canadian IT industries with more effective algorithms for data dependence analysis.

随着网络技术的飞速发展，互联网已经成为现代社会不可或缺的一部分。在不到50年的时间里，互联网不仅成为娱乐、通信和社交网络的主要平台，而且成为许多新业务服务的主要驱动力。在互联网上的各种应用程序的引擎盖下是支持其平稳运行的核心数学模型和算法。改进核心模型和算法将对整个互联网生态系统产生重大影响。因此，我们有动力研究对许多互联网应用程序未来成功至关重要的基本模型。 回顾历史，我们发现在互联网的每一个研发阶段，都提出了各种各样的依赖模型。从理论上讲，Internet流量相关模型和拥塞控制理论为现代计算机网络的理论性能分析奠定了基础。在实践中，依赖模型是流量工程、网络容量规划和基于预测的资源配置的基础。总体而言，互联网本质上依赖于各种依赖模型。 虽然在计算机和通信网络的依赖建模方面已经做了大量的工作，但网络流量中丰富的依赖结构，特别是那些超出线性依赖范围的依赖结构，还没有得到充分的研究。这个NSERC发现提案的目标是通过探索Copula的力量来推动最先进的技术。Copula是一种依赖模型，能够捕捉网络流量之间/内部的函数依赖关系，并在流量预测，自动资源调整和网络采样方面显示出良好的效果。通过数学分析，仿真和真实世界的实验，该项目将解决两个交织的研究主题：基于copula的网络性能断层扫描和相互依赖网络的copula模型。在第一个主题中，我们将开发一个可靠的网络断层扫描方法，可以推断网络性能的基础上，从少量的监测点的观察。在第二个主题中，我们将研究相互依赖的网络系统，其中一个网络的性能取决于其他网络的性能。 这项研究将丰富计算机和通信网络的依赖建模工具集，并将有利于许多现实世界的应用，包括例如实时多媒体传输，大规模在线广播的性能监控，智能电网通信。通过将理论成果应用到我们与加拿大工业合作伙伴的现有项目中，我们的研究成果可以使加拿大IT行业受益，为数据依赖分析提供更有效的算法。