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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668976
• 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.

随着网络技术的快速发展，互联网已成为现代社会必不可少的一部分。在不到50年的时间里，互联网不仅成为娱乐，沟通和社交网络的主要平台，而且还成为许多新商业服务的主要推动力。在互联网上的不同应用程序的引擎盖下是核心数学模型和算法，这些模型和算法支持其平稳的操作。改善核心模型和算法将对互联网的整个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，我们发现在互联网研究和开发的每个阶段都提出了各种依赖模型。从理论上讲，互联网交通依赖模型以及排队理论为现代计算机网络的理论性能分析奠定了基础。实际上，依赖模型是交通工程，网络容量计划和基于预测的资源供应的基础。总体而言，互联网基本上依赖各种依赖模型。 ******虽然已经对计算机和通信网络的依赖性建模进行了广泛的工作，但网络流量中丰富的依赖性结构，尤其是线性依赖范围之外的依赖性结构，但尚未得到充分探索。这项NSERC发现建议的目的是通过探索Copulas的力量来推动最先进的事情。 Copulas是依赖模型，能够捕获网络流量流中/内部之间的功能依赖性，并显示出令人鼓舞的流量预测结果，自动资源调整和网络采样。通过数学分析，仿真和现实世界实验，该项目将介绍两个相互交织的研究主题：基于Copula的网络性能断层扫描和相互依存网络的Copula模型。在第一个主题中，我们将开发一种可靠的网络断层扫描方法，该方法可以根据少量监视点的观察来推断网络性能。在第二个主题中，我们将研究相互依存网络的系统，其中一个网络的性能取决于其他网络的性能。 ******研究将丰富计算机和通信网络依赖建模的工具集，并将受益于许多现实世界应用程序，包括实时多媒体交付，大规模在线广播的性能监控以及智能电网通信。通过将理论结果应用于加拿大工业合作伙伴的现有项目，我们的研究结果可以使加拿大IT Industries具有更有效的数据依赖性分析算法。