Cyberinfrastructure, Science Gateways, Campus Bridging, and Cloud Computing

Cyberinfrastructure, Science Gateways, Campus Bridging, and Cloud Computing

Craig A. Stewart (Indiana University, USA), Richard Knepper (Indiana University, USA), Matthew R. Link (Indiana University, USA), Marlon Pierce (Indiana University, USA), Eric Wernert (Indiana University, USA) and Nancy Wilkins-Diehr (University of California, San Diego, USA)
Copyright: © 2015 |Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch645

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The evolution of cyberinfrastructure as a concept spans some three decades. The earliest references in 1976 (Sorkin, 2006) and in a Clarke and Hunker press briefing (1998) mention “cyber-infrastructure” in the context of cyber threats and cybersecurity.

Cyberinfrastructure in today’s sense originated in the NSF-funded supercomputer centers program of the 1980s (National Science Foundation, 2006). The NSF centers delivered and supported supercomputers, which were generally accessed individually, often with users logging into a system that served as a front end to such supercomputers. Using multiple supercomputers in concert was at first practically impossible. This began to change in the late 1980s. Projects such as the CASA testbed, (Messina, 1991a, 1991b) linked multiple supercomputers together to support distributed scientific workflows. The NASA Information Power Grid (Johnston, Vaziri, & Tanner, 2001) provided a production grid of multiple supercomputers connected by a high-speed network.

These two projects advanced the grid concept in computer science and computational science. The computing architecture implied in the term made intuitive sense. An early definition of grid computing reads:

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. (Foster & Kesselman, 1998)

Other grid types based on function include data grids and collaboration grids. Semantic grids and peer-to-peer systems are grids distinguished by the characteristics of the protocols and interactions between components (Fox, 2006).

At the turn of the century two major projects developed major grid infrastructure in the USA. Three different projects developing grid technology to analyze data for physics data led to today’s Open Science Grid (Open Science Grid, 2013). In 2001, the NSF funded the TeraGrid, computational, storage, and visualization resources in a grid that spanned the US.

The term “cyberinfrastructure” in its sense of knowledge infrastructure was introduced in 2001 by Dr. Ruzena Bajcsy in her charge to a National Science Foundation Advisory Panel led by Dr. Daniel Atkins. She wished to “create a program on cyberinfrastructure that would involve the broader computer science/information technology community” (Bajcsy, 2013). According to Freeman (2007) this effort “led to the creation of a term for infrastructure that attempts to capture the integration of computing, communications, and information for the support of other activities (especially scientific in the case of NSF).” The NSF report created by the Atkins-led NSF Advisory Panel “Revolutionizing Science and Engineering Through Cyberinfrastructure,” now known as “the Atkins report,” clarified: “The newer term cyberinfrastructure refers to infrastructure based upon distributed computer, information and communication technology. If infrastructure is required for an industrial economy, then we could say that cyberinfrastructure is required for a knowledge economy” (Atkins et al., 2003a).

Indiana University staff developed a definition more specific in terms of identifying components and function.

Key Terms in this Chapter

Citizen Science: The work of individuals or teams of amateur, non-professional, or volunteer scientists who conduct research, gather and analyze data, perform pattern recognition, and develop technology, often in support of professional scientists.

Scientific Workflows: Sets of tasks done in a specific order during computational experiments. Tasks are usually scientific applications that may run on more than one resource in the cyberinfrastructure. Workflows accommodate conditional decisions, loops, and interactivity with human monitors at various stages.

Cloud Computing: Aims to deliver on-demand, affordable access to a distributed, shared pool of computing and storage resources, applications, and services usually via the Internet to a large number of users.

Cyberinfrastructure: Cyberinfrastructure consists of computational systems, data and information management, advanced instruments, visualization environments, and people, all linked together by software and advanced networks to improve scholarly productivity and enable knowledge breakthroughs and discoveries not otherwise possible.

Science Gateways: Community-developed tools, applications, and data integrated via a portal or a suite of applications, usually in a graphical user interface, and customized to the needs of specific communities.

E-Science: Computationally intensive science carried out through distributed global collaborations enabled by the Internet, involving access to large data collections, very large scale computing resources and high performance visualization.

Campus Bridging: The seamlessly integrated use of cyberinfrastructure operated with other local or remote cyberinfrastructure as if they were proximate to the user.

Computational Grid: Hardware and software infrastructure that provides access to geographically distributed computational resources. Data grids focus on data analysis. Both can be components of cyberinfrastructure.

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