CIF:Small:Network Tomography and Resource Allocation

CIF：小：网络断层扫描和资源分配

• 批准号: 1717033
• 负责人: Brian Mark
• 金额: $ 50万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717033&HistoricalAwards=false
• 关键词: CIF; Small; Network; Tomography; Resource

Part 1.Communication networks, including the Internet and cellular networks, provide an infrastructure for information and data exchange that is critical to modern society. As networks have increased in size and complexity to meet the growing demands of users and new multimedia applications, they have also become more difficult to manage. Consequently, evaluating the performance of a network and allocating resources to improve performance are extremely challenging tasks. This project will develop an approach to evaluate the performance of a network through endpoint traffic measurements and by sending probe packets through the network. This approach is referred to as network tomography, since the concept is similar to medical tomography, whereby an image of an organ is reconstructed through virtual sectioning using some form of penetrating wave, e.g., X-rays or ultrasound. Current methods for network tomography do not scale well with the size of the network and cannot provide estimates of network performance in real-time. The project will investigate new methods that will be capable of evaluating the performance of large networks in real-time through endpoint measurements and statistical analysis. This information will in turn be used to allocate resources within the network in order to enhance network performance. The ultimate goal of the project is to develop a framework, based on new methodologies for network tomography, to alleviate congestion and service degradation in communication networks such as the Internet, data storage networks, future cellular networks, as well as transportation networks. The project will advance the field of networking via the development of real-time and scalable methods for network tomography and resource allocation, which will impact society through significant improvements in the performance, efficiency, and reliability of communication and transportation networks. The project will also involve the development of an educational software tool to simulate and graphically depict the operation of a network, with knobs to allow the user to control the allocation of network resources and visualize the effect of such allocations on network performance. Students, including some from underrepresented groups, will gain valuable experience from implementing algorithms, running computer simulations, and conducting empirical validation with real data from the Internet and cellular networks.Part 2:Performance evaluation and resource allocation of communication networks are extremely challenging tasks due to the tremendous scale and complexity of modern networks. Traditional approaches to network performance evaluation include queueing theory and computer simulation. In this project, a framework for network tomography of both traffic rate and delay on network links from endpoint traffic measurements, will be developed and investigated, with application to network resource allocation. Gallager's model for minimum delay routing in a network will be used to formulate the joint problem of traffic rate and delay estimation, and derive information that can be applied directly to resource allocation. A new approach to traffic rate tomography, which has the potential to improve upon the accuracy, computational efficiency, and generality of earlier methodologies will be explored. A recent approach to delay network tomography based on parameter estimation of a partially observable bivariate Markov chain model will be further developed in the context of the proposed framework. A major focus of the project will be on developing efficient online algorithms for network tomography and resource allocation that can be applied to improve network performance in near real-time.The proposed investigations are grounded in the theory and estimation of multivariate Markov processes, and explore the interplay between the existing theories of queueing networks and statistical inference. The research will involve the development of new models for network tomography, recursive estimation algorithms, and empirical validation. The proposed research will be applicable to the performance evaluation of a wide range of networks, including computer networks, cellular networks, and transportation networks. The research will contribute to new mechanisms to improve the quality-of-service and quality-of experience for users of the Internet and next generation wireless infrastructure. The proposed approach to network tomography will also help optimize the planning of transportation systems.

第1部分通信网络，包括互联网和蜂窝网络，为信息和数据交换提供了对现代社会至关重要的基础设施。随着网络的规模和复杂性不断增加，以满足用户和新的多媒体应用的日益增长的需求，它们也变得更加难以管理。因此，评估网络性能并分配资源以提高性能是极具挑战性的任务。该项目将开发一种方法，通过终端流量测量和通过网络发送探测数据包来评估网络性能。这种方法被称为网络层析成像，因为该概念类似于医学层析成像，通过使用某种形式的穿透波，例如X射线或超声波，通过虚拟切片重建器官的图像。当前的网络层析成像方法不能很好地适应网络的规模，并且不能实时提供对网络性能的估计。该项目将研究能够通过端点测量和统计分析实时评估大型网络性能的新方法。该信息又将用于分配网络内的资源，以提高网络性能。该项目的最终目标是根据网络层析成像的新方法开发一个框架，以缓解互联网、数据存储网络、未来蜂窝网络以及交通网络等通信网络中的拥堵和服务退化。该项目将通过开发实时和可扩展的网络断层扫描和资源分配方法来推动网络领域的发展，这将通过显著提高通信和运输网络的性能、效率和可靠性来影响社会。该项目还将涉及开发一种教育软件工具，以模拟和图形化地描述网络的运行，并使用旋钮使用户能够控制网络资源的分配，并直观地显示这种分配对网络性能的影响。学生，包括一些来自代表性不足的群体，将从实施算法、运行计算机模拟和使用来自互联网和蜂窝网络的真实数据进行经验验证中获得宝贵的经验。第2部分：由于现代网络的巨大规模和复杂性，通信网络的性能评估和资源分配是极具挑战性的任务。传统的网络性能评估方法包括排队论和计算机模拟。在这个项目中，将开发和研究一个用于根据终端流量测量来对网络链路上的流量速率和延迟进行网络层析的框架，并将其应用于网络资源分配。Galager的网络最小时延路由模型将被用来描述通信量速率和时延估计的联合问题，并推导出可直接应用于资源分配的信息。将探索一种新的交通速率层析成像方法，该方法具有改进早期方法的准确性、计算效率和通用性的潜力。基于部分可观测的双变量马尔可夫链模型的参数估计的延迟网络层析成像方法将在所提出的框架的背景下进一步发展。该项目的一个主要焦点将是开发高效的在线网络层析成像和资源分配算法，以提高近实时的网络性能。所提出的研究基于多变量马尔可夫过程的理论和估计，并探索现有排队网络理论和统计推理之间的相互作用。这项研究将涉及网络层析成像新模型的开发、递归估计算法和经验验证。建议的研究将适用于广泛的网络性能评估，包括计算机网络、蜂窝网络和交通网络。这项研究将有助于建立新的机制，以改善互联网和下一代无线基础设施用户的服务质量和体验质量。提出的网络层析成像方法也将有助于优化交通系统的规划。

项目成果

Traffic Workload Envelope for Network Performance Guarantees with Multiplexing Gain," IEEE Globecom 2022, Rio De Janeiro, Brazil, Dec. 2022.

具有复用增益的网络性能保证的流量工作负载范围”，IEEE Globecom 2022，巴西里约热内卢，2022 年 12 月。

• 发表时间: 2022
• 期刊: IEEE Global Communications Conference
• 作者: Kordi Boroujeny, Massieh;Mark, Brian L.;Ephraim, Yariv
• 通讯作者: Ephraim, Yariv

Phase-type bounds on network performance

网络性能的阶段类型界限

• DOI: 10.1109/ciss.2018.8362284
• 发表时间: 2018
• 期刊: 52nd Annual Conference on Information Sciences and Systems (CISS
• 作者: Boroujeny, Massieh Kordi;Ephraim, Yariv;Mark, Brian L.
• 通讯作者: Mark, Brian L.

M. Kordi Boroujeny and B.L. Mark, "A Framework for Providing Stochastic Delay Guarantees in Communication Networks," IEEE ICC 2021 Workshop on Time-sensitive and Deterministic Networking, Montreal, Canada, June 2021

M. Kordi Boroujeny 和 B.L.

• 发表时间: 2021
• 期刊: IEEE ICC 2021 Workshop on Time-sensitive and Deterministic Networking
• 作者: Kordi Boroujeny, Massieh;Mark, Brian L.
• 通讯作者: Mark, Brian L.

Stochastic Traffic Regulator for End-to-End Network Delay Guarantees

用于端到端网络延迟保证的随机流量调节器

• DOI: 10.1109/icc40277.2020.9149446
• 发表时间: 2020
• 期刊: ICC 2020 - 2020 IEEE International Conference on Communications (ICC
• 作者: Boroujeny, Massieh Kordi;Mark, Brian L.;Ephraim, Yariv
• 通讯作者: Ephraim, Yariv

Traffic Rate Network Tomography via Moment Generating Function Matching

通过矩生成函数匹配的流量网络断层扫描

• 发表时间: 2022
• 期刊: Conference on Information Sciences and Systems
• 作者: Ephraim, Yariv;Coblenz, Joshua;Mark, Brian L.;Lev-Ari, Hanoch
• 通讯作者: Lev-Ari, Hanoch