Cyber Infrastructure as a Service to Empower Multidisciplinary, Data-Driven Scientific Research

In supporting its large scale, multidisciplinary scientific research efforts across all the university campuses and by the research personnel spread over literally every corner of the state, the state of Nevada needs to build and leverage its own Cyber infrastructure. Following the well-established as-a-service model, this state-wide Cyber infrastructure that consists of data acquisition, data storage, advanced instruments, visualization, computing and information processing systems, and people, all seamlessly linked together through a high-speed network, is designed and operated to deliver the benefits of Cyber infrastructure-as-a- Service (CaaS).There are three major service groups in this CaaS, namely (i) supporting infrastructural services that comprise sensors, computing/storage/networking hardware, operating system, management tools, virtualization and message passing interface (MPI); (ii) data transmission and storage services that provide connectivity to various big data sources, as well as cached and stored datasets in a distributed storage backend; and (iii) processing and visualization services that provide user access to rich processing and visualization tools and packages essential to various scientific research workflows. Built on commodity hardware and open source software packages, the Southern Nevada Research Cloud(SNRC)and a data repository in a separate location constitute a low cost solution to deliver all these services around CaaS. The service-oriented architecture and implementation of the SNRC are geared to encapsulate as much detail of big data processing and cloud computing as possible away from end users; rather scientists only need to learn and access an interactive web-based interface to conduct their collaborative, multidisciplinary, data-intensive research. The capability and easy-to-use features of the SNRC are demonstrated through a use case that attempts to derive a solar radiation model from a large data set by regression analysis.