MRI: Enabling Research on Terabyte-Scale Datasets

该项目将获得一个集群，对信息检索、网络流量分析、入侵检测和生物医学图像处理领域的太字节规模问题进行研究，通过向用户提供长时间的太字节存储，实现长期研究。在这个共享设施中，调查人员将合作利用一个包含20tb磁盘存储的集群。此外，通过光纤通道连接的高性能tb磁盘子系统将作为大型磁盘存储的高速缓存。为了完成内存存储，数据迁移将利用现有的三级存储备份系统。为了在处理如此大的数据集时获得最高的效率和最高的灵活性，集群采用四处理器节点和每个节点8 GB的RAM。对全尺寸数据的测试消除了在许多领域取样较小数据集所造成的错误。支持新的研究，项目范围从分析分布式拒绝服务，通过学习生物医学图像，到并行断层合成。在这个设施上需要改进的软件工具包括信息检索、支持向量机（SVM）、聚类、网络模拟、核外搜索、三维图像重建和时空数据库的方法。预计数据操作能力的增加将提供更快的周转时间，并可能实现目前安装的技术无法实现的结果。

This project, acquiring a cluster to perform research on terabyte-scale problems in the areas of information retrieval, network traffic analysis, intrusion detection, and image processing for biomedicine, enables long term studies by providing access to users with terabytes of storage for extended periods of time. In this shared facility, investigators will cooperatively exploit a cluster containing 20 terabytes of disk storage. Additionally, a high performance terabyte disk subsystem, connected by fibre channel will serve as a higher speed cache for the large disk storage. To complete the memory storage, data migration will leverage using existing tertiary storage backup systems. In order to attain both the highest efficiency and the highest flexibility in processing such large datasets, the cluster employs quad-processor nodes and 8 GB of RAM per node. Testing on full size data eliminates errors caused by sampling smaller datasets in many areas. Enabling new research, the projects range from analysis of distributed denial of service, through learning for biomedical images, to parallel tomosynthesis. Software tools to be refined on this facility include methodologies for Information Retrieval, Support Vector Machines (SVM), Clustering, Network Simulation, Out-of-Core Search, 3-D Image Reconstruction, and Spatial and Temporal Databases. It is anticipated that the increase in data manipulation capabilities will provide much quicker turn-around and make possible results that are inaccessible with the currently installed technology.