Design, Analysis, and Monitoring of Network Characteristics Using Tomography Techniques

PI和合作PI: Michailidis， George和Nair， Vijay机构：密歇根大学安娜伯分校标题：使用断层扫描技术的网络特性的设计、分析和监测本研究项目是由在评估和监测计算机和通信网络的服务质量（QoS）特性中出现的问题所激发的。它侧重于主动网络断层扫描，其中端到端流量测量是通过主动探测网络获得的。该项目研究了许多重要而有趣的统计问题，这些问题涉及使用活动断层扫描对网络数据进行收集、建模和分析。具体地说，它引入了一种新的、灵活的探测实验，它比目前的方案有优势。研究人员将研究与高效柔性广播探测实验设计相关的几个研究问题：可识别性问题，高效设计的构建，测试探针的最佳分配（根据一些合适的度量），以及检测和定位网络问题的实时监控策略。研究的主要内容是对具有缺失数据的大规模逆问题进行估计和推理。重点是用于估计丢包率和延迟分布及其渐近性质的快速可扩展算法。此外，还将介绍捕捉网络流量主要特征的时空依赖性新模型，并分析这些模型下各种估计方法的行为。最后，本研究计划的一个重要组成部分是在实际数据上验证所提出的模型和技术。当今的计算机网络已经发展成为大型、复杂、分散的系统。人们对评估关键网络性能参数和服务质量指标（如流量强度、链路延迟和丢包率）以及识别瓶颈节点和检测网络故障（以连接、路由故障和恶意活动的形式）非常感兴趣。然而，缺乏集中控制使得网络性能的定量评估成为一项相当困难的任务，因为详细的排队和流量模型不能很好地捕捉它们的复杂性和特征。网络断层扫描能够评估大规模网络的性能，并将异常行为定位到单个组件和子网络。从拟议的研究中获得的理解和见解将导致基本原则的核心和关键统计方法和技术的工具包，用于分析和设计网络监测计划。通过将必要的信息整合到准入、流量和路由控制协议中，高效和可扩展的网络监控可以显著地有利于网络控制和供应。提出的技术和算法将集成到一个名为FLEXICAST的开源工具中。该研究计划也将纳入统计学系的教育活动。计划的努力包括培训研究生和本科生，开发新课程，促进与工程专业学生和教师的合作，以及与网络社区的互动，以帮助将研究成果转化为工业。

Abstract Prop ID: DMS-0505535 PI and co-PI: Michailidis, George and Nair, Vijay Institution: University of Michigan Ann Arbor Title: Design, Analysis, and Monitoring of Network Characteristics Using Tomography Techniques This research program is motivated by problems that arise in assessing and monitoring the quality of service (QoS) characteristics of computer and communication networks. It focuses onactive network tomography where end-to-end traffic measurements are obtained by actively probing the network. The project examines a number of important and interesting statistical issues dealing with the collection, modeling, and analysis of network data using active tomography. Specifically, it introduces a new, flexible class of probing experiments that has advantages over the current schemes. The investigators will study several research issues related to the design of efficient flexicast probing experiments: questions of identifiability, construction of efficient designs, optimal allocation of test probes (according to some suitable metric), and real-time monitoring strategies to detect and localize network problems. A substantial portion of the research deals with estimation and inference for a large-scale inverse problem with missing data. The focus is on fast and scalable algorithms for estimating packet loss rates and delay distributions and their asymptotic properties. Furthermore, new models for spatial and temporal dependence that capture the main features of network traffic will be introduced and the behavior of various estimation methods under these models will be analyzed. Finally, an important component of this research program is the validation of the proposed models and techniques on real data. Present day computer networks have evolved into large, complex, decentralized systems. There is a great deal of interest in estimating key network performance parameters and quality of service indicators, such as traffic intensities, link delays and dropped packet rates, and in identifying bottleneck nodes and detecting network failures in the form of connectivity, routing faults and malicious activity. However, the lack of centralized control makes the quantitative assessment of network performance a rather difficult task, since detailed queueing and traffic models do not capture their complexity and characteristics well. Network tomography is capable of assessing the performance of large-scale networks and in localizing anomalous behavior to individual components and subnetworks. The understanding and insights gained as a result of the proposed research will lead to a core of basic principles and a toolkit of key statistical methods and techniques for the analysis and design of network monitoring schemes. Efficient and scalable network monitoring could significantly benefit network control and provisioning by incorporating the necessary information into admission, flow and routing control protocols. The proposed techniques and algorithms will be integrated into an open source tool, called FLEXICAST. The research program will also be integrated into the educational activities of the Department of Statistics. Planned efforts include training of graduate and undergraduate students, the development of a new course, stimulation of collaborations with students andfaculty in Engineering, and interaction with the networking community to help transfer the research results to industry.