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.

在此项目中，我们解决了与为系统支持时变化数据的远程可视化相关的问题。大多数现有的远程可视化系统都采用了客户端服务器模型。由于集中式数据访问和可视化计算，该服务器可能成为瓶颈。此外，由于要传输大量可视化数据，因此可以轻松地重载网络。为了克服这些问题，研究人员提出了各种方法，通过将工作量转移到客户端来分散远程可视化计算。但是，由于客户端92S计算功率通常受到限制，因此这种工作负载迁移的效果受到限制。 减少服务器瓶颈的另一个替代方法是在网络中部署多个服务器。但是，多个服务器的部署和管理非常昂贵，可能会引发一些政策问题。我们的目标是开发可扩展，轻巧且易于部署的体系结构，该体系结构能够提供一定程度的服务质量（QOS）来远程对时间变化数据的远程可视化。为了实现这一目标，我们提出了一个基于系统的对等（P2P）体系结构。一般而言，P2P系统中的每个节点都被认为是平等的，因此该模型在技术上与客户服务器一个节点不同。 我们体系结构的亮点是（1）服务器数据，工作负载分配给Internet中愿意共享资源的志愿者节点组。这些节点构成了多个虚拟服务器。 （2）允许客户通过任何cast服务访问服务器组。 Anycast会自动找到组中最佳的服务器，并将数据返回到请求客户端。 最好的服务器应该提供最佳的服务质量（即，就数据访问的最小延迟而言）。 （3）时空多分辨率数据结构，称为时空分区（TSP）树，其中存储了多个分辨率块，并且子树上的数据彼此独立。 通过在树中存储多个分辨率块，可以在选择最佳服务器的选择时进行更多选择，该服务器可能会随系统动态而变化。在该项目中，我们计划完全设计，分析，实现和评估基于P2P的结构，以远程可视化时间变化数据。我们将执行以下研究任务（1）在覆盖网络中QoS了解的任何广播服务的设计，分析和实施； （2）增强TSP树结构，尤其是其编码方案的数据； （3）有效算法的设计，评估和实现，用于分布志愿者节点之间的时变数据； （4）基于提议的体系结构的原型系统的构建和评估。