MRI: Acquisition of an Adaptive Data Cluster for Data-intensive Applications in Science and Engineering

点击翻译按钮获取中文摘要

This project, acquiring an adaptive multi-petabyte scalable storage cluster for high-end applications, aims to service multiple research areas such as: genomics, bioinformatics, computer security, digital ethnography, environmental modeling, and computer science. The flexible storage consists of an integrated multi-petabyte disk system with integrated compute capabilities and a scalable tape archive system for data and storage intensive applications. The system allows I/O-limited calculations to be performed directly on the storage nodes (unlike the traditional storage clusters) and can also act as a distributed file system with massive bandwidth to allow CPU-limited calculations to benefit from existing cluster computational resources (unlike typical Hadoop clusters). The instrument enables - Looking deeper in modeling genomes, hyper-extractive economies and phenotyping- Bringing greater data capacity tied to computational resourcing,- Attacking grand challenges (e.g., forecasting responses of ecological systems to natural and anthropogenic global and regional change).- Developing new algorithms in high-throughput phenotyping, computer security, and genome annotation (hence enabling a new science with the hybrid platform).Thus, the storage cluster constitutes a seminal component for a critical need, a campus-based facility for data immersive computing, not only at the institution, but for the entire state, since the institution currently does not have a central facility with capability for 'big data' high-end computing. As new algorithms are developed for better modeling cyber interactions that can lead to increase the financial and network infrastructure's ability, resiliency, and resistance, multidisciplinary impacts on cybersecurity research are likely to be felt. It can also contribute to train a new generation of researchers in tools and techniques for data-intensive computing and ease their migration to XSEDE when their research needs exceed the local resources. It can contribute to protect the environment when developing better models of the interaction of water, ecology, and economic factors. Moreover, it can enhance and integrate educational efforts at the K-12, undergraduate, and graduate levels in bioinformatics (e.g., preparation of educational materials, impacting the K-12 and STEM education such as 'It's a BLAST' and GROW workshops for female high school students). Ultimately, it allows access to community colleges and non-PhD granting institutions to extend big data through and EPSCoR state, enabling many researchers and educators.