Collaborative Research: Visualization: Overlay Network Support for Remote Visualization of Time-Varying Data

协作研究：可视化：覆盖网络支持时变数据的远程可视化

• 批准号: 0329181
• 负责人: Wei Zhao
• 金额: --
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0329181&HistoricalAwards=false
• 关键词: Collaborative; Research; Visualization; Overlay; Network

In this project, we address issues related to providing system support for remote visualization of time-varying data. A majority of the existing remote visualization systems have adopted the client-server model. The server may become a bottleneck due to the centralized data access and visualization computation. In addition, the network can be easily overloaded because of the large volume of visualization data to be transmitted. To overcome these problems, researchers have proposed various methods to decentralize the remote visualization computation by shifting workload to the clients. The effect of such a workload migration however, is limited since the client 92s computation power is often constrained. Another alternative to reduce the server bottleneck is to deploy multiple servers in the network. However, deployment and management of multiple servers is very expensive, and may raise some policy issues.Our objective is to develop a scalable, lightweight, and easily deployable architecture, which is capable of providing a certain degree of quality of services (QoS) to remote visualization of time-varying data. To achieve this objective, we propose a Peer-to-peer (P2P) system-based architecture. Generally speaking, each node in a P2P system is considered equal, and hence this model is technically different from the client-server one. The highlights of our architecture are (1) Server data and workload are distributed to the groups of volunteer nodes in the Internet which are willing to share their resources. These nodes compose multiple virtual servers. (2) The client is allowed access to the server group via anycast service. Anycast automatically finds the best server in the group, and returns the data to the requesting client. The best server is supposed to provide the best quality of services (i.e. in terms of the minimum delay in data access). (3) A spatio temporal multi-resolution data structure, called Time-Space Partitioning (TSP) tree, where multiple resolution blocks are stored and the data on the sub-trees are independent of each other, is employed. By storing multiple resolution blocks in the tree, a client is permitted more choices in making the selection of best server, which may vary with the system dynamics.In this project, we plan to fully design, analyze, implement, and evaluate the P2P-basedarchitecture for remote visualization of time-varying data. We will carry out the following research tasks (1) design, analysis, and implementation of QoS-aware anycast services in the overlay networks; (2) enhancement of the TSP tree structure, especially its data encoding schemes; (3) design, evaluation, and realization of an efficient algorithm for distribution of time-varying data among the volunteer nodes; (4) construction and evaluation of a prototype system based on the proposed architecture.

在这个项目中，我们解决相关的问题，提供系统支持的时变数据的远程可视化。现有的远程可视化系统大多采用客户机-服务器模型。由于集中的数据访问和可视化计算，服务器可能成为瓶颈。此外，由于要传输大量的可视化数据，网络很容易过载。为了克服这些问题，研究人员提出了各种方法来分散远程可视化计算的工作量转移到客户端。然而，这种工作负载迁移的效果是有限的，因为客户端92的计算能力通常受到约束。 另一种减少服务器瓶颈的方法是在网络中部署多个服务器。然而，部署和管理多个服务器是非常昂贵的，并可能会引起一些政策issues. We的目标是开发一个可扩展的，轻量级的，易于部署的架构，这是能够提供一定程度的服务质量（QoS）的时变数据的远程可视化。为了实现这一目标，我们提出了一个对等（P2P）系统为基础的架构。一般来说，P2P系统中的每个节点都被认为是平等的，因此这种模型在技术上不同于客户端-服务器模型。 该体系结构的主要特点是：（1）将服务器数据和工作负载分配给愿意共享资源的志愿者节点。这些节点组成多个虚拟服务器。 (2)允许客户端通过任播服务访问服务器组。 Anycast会自动找到组中最好的服务器，并将数据返回给请求客户端。 最好的服务器应该提供最好的服务质量（即在数据访问的最小延迟方面）。(3)采用时空划分（TSP）树的时空多分辨率数据结构，其中存储多个分辨率块，子树上的数据彼此独立。 通过在树中存储多个分辨率块，允许客户端在选择最佳服务器时有更多的选择，这可能会随着系统动态而变化。在本项目中，我们计划全面设计，分析，实现和评估基于P2P的时变数据远程可视化体系结构。本论文的主要研究工作包括：（1）在覆盖网络中设计、分析和实现QoS感知的选播服务;（2）改进TSP树结构，特别是其数据编码方案;（3）设计、评估和实现一种有效的时变数据在志愿节点之间的分发算法;（4）设计、评估和实现一种基于QoS感知的选播服务。（4）基于该体系结构的原型系统的构建与评价。