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

在这个项目中，我们解决了与为时变数据的远程可视化提供系统支持相关的问题。现有的远程可视化系统大多采用客户-服务器模式。由于集中的数据访问和可视化计算，服务器可能会成为瓶颈。此外，由于要传输的可视化数据量很大，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，通过将工作量转移到客户端来分散远程可视化计算。然而，这种工作负载迁移的效果是有限的，因为客户端92的计算能力通常是受限的。另一种减少服务器瓶颈的方法是在网络中部署多台服务器。然而，部署和管理多个服务器的成本非常高，并且可能会引发一些策略问题。我们的目标是开发一个可扩展、轻量级、易于部署的体系结构，该体系结构能够为时变数据的远程可视化提供一定程度的服务质量(Qos)。为了实现这一目标，我们提出了一种基于P2P系统的体系结构。一般来说，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不同于客户-服务器模型。该体系结构的亮点在于：(1)将服务器的数据和工作负载分配给互联网上愿意共享资源的志愿者节点组。这些节点组成多个虚拟服务器。(2)允许客户端通过选播服务访问服务器组。Anycast自动查找组中最好的服务器，并将数据返回给发出请求的客户端。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)采用一种时空多分辨率数据结构，称为时空分割(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，客户可以有更多的选择来选择最佳服务器，这可能会随着系统的动态变化而变化。在这个项目中，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。我们将开展以下研究工作：(1)覆盖网络中支持服务质量的选播服务的设计、分析和实现；(2)改进TSP树结构，特别是其数据编码方案；(3)设计、评估和实现一种高效的时变数据在志愿节点间的分配算法；(4)基于所提出的体系结构构建和评估一个原型系统。

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.