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Cyber-Infrastructure in Education. Geoffrey Fox Director, Digital Science Center, Pervasive Technology Institute Associate Dean for Research and Graduate Studies,  School of Informatics and Computing

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cyber infrastructure in education

Cyber-Infrastructure in Education

Geoffrey Fox

  • http://www.infomall.org
  • Director, Digital Science Center, Pervasive Technology Institute
  • Associate Dean for Research and Graduate Studies,  School of Informatics and Computing
  • Indiana University Bloomington

South Carolina State University

Cyberinfrastructure Day

March 3 2011

types of activities
Types of Activities
  • Cyberinfrastructure ranges from a web page through a petaflop supercomputer
  • Research has substantial needs such as either
    • Petaflop supercomputer
    • Ability to analyze many (upto 100 now) terabytes of data (on a cloud)
  • Education needs
    • Access to results of cyberinfrastructure research
    • Broad access to scholarly information (digital library)
    • Teach students about e-Science (domain science) and Cyberinfrastructure (Computer Science)
    • Exploit electronic infrastructure to enhance learning
access to results of cyberinfrastructure research
Access to results of cyberinfrastructure research
  • Portals are the access points to electronic resources
    • For example is an access point to an electronic shop
  • e-Science projects have a portal interface for their scientists
    • Some have education components
    • Some interest in producing education oriented interfaces by outsiders but no clear initiative?
broad access to scholarly information digital library
Broad access to scholarly information (digital library)
  • National Science Digital Library
  • There is an interesting discussion of role of University libraries in preservation of “data produced by faculty”
  • Curriculum libraries such as that at MIT or HPCUniversity
  • Collections of articles maintained by publishers and professional societies have problems due to charges
    • Role of centralized and de-centralized collections still not agreed
  • Google (for example) is keen to “own all data” including digital books and even science data if can be linked to Google Earth!
    • But this has opposite problem of preserving Intellectual property (seen clearly in music piracy)
  • Note MapReduce perfect for analyzing such data
teach students about e science domain science and cyberinfrastructure computer science
Teach students about e-Science (domain science) and Cyberinfrastructure (Computer Science)
  • This can be quite sophisticated as in difficult parallel algorithms
  • As in portals, one can leverage research investments
  • Does not need students to run petaflop simulations
    • Should be able to capture essence of computational/science issues in smaller runs
    • Appliances (see later) can be used
  • FutureGrid possible site
  • Note clouds very popular with students as many commercial jobs in development and use companies
    • As well as for CS research and as vehicle for domain science
exploit electronic infrastructure to enhance learning
Exploit electronic infrastructure to enhance learning
  • Several quite old approaches are critical and dominant
    • “Just a bunch of web pages” aka digital library
    • Video conferencing
    • Shared material as in Webex, Adobe Connect
  • Note asynchronous interaction via Twitter, Blackboard, Google docs etc. much easier (and successful) than synchronous (Polycom, access grid, Webex) approaches
  • Interactive web learning environments such as
  • Virtual worlds such as Second Life have not taken off but some think this will change as performance of clients and networks are improving dramatically (VRML failed ~1999)
  • Must move to an environment consistent with world view of current students aka the “Twitter University”
c4 continuous collaborative computational cloud
C4 = Continuous Collaborative Computational Cloud

C4 Education Vision

C4 Education will exploit advanced means of communication, for example, “Tabatar” conference tables as clients , with real-time language translation, contextual awareness of speakers, support for people with disabilities; servers supporting collaboration between learners and teachers through “virtual worlds” generalizing Twitter Clouds with MapReduce frontends, Second Life ……


While the internet has changed the way we communicate and get entertainment, we need to empower the next generation of engineers and scientists with technology that enables interdisciplinary collaboration for lifelong learning.

Today, the cloud is a set of services that people explicitly have to access (from laptops, desktops, etc). In 2020 the C4 will be part of our lives, as a larger, pervasive, continuous experience. The measure of success will be how “invisible” it becomes.

C4 Society Vision

We are no prophets and can’t anticipate what exactly will work, but we expect to have high bandwidth and ubiquitous connectivity for everyone everywhere, even in rural areas (using power-efficient micro data centers the size of shoe boxes). Here the cloud will enable business, fun, destruction and creation of regimes (societies)

higher education 2020
Higher Education 2020

Computational Thinking


& Simulation







C4 Intelligent People

C4 Intelligent Economy

C4Intelligent Society









Internet &




MotivatingIssues job / education mismatch

Higher Ed rigidity

Interdisciplinary work

Engineering v Science, Little v. Big science





Educate “Net Generation”

Re-educate pre “Net Generation”

in Science and Engineering

Exploiting and developing C4

C4Curricula, programs

C4 Experiences (delivery mechanism)

C4 REUs, Internships, Fellowships

CDESE is Computational and Data-enabled Science and Engineering

educational appliances
Educational appliances
  • One component of C4
  • A flexible, extensible platform for hands-on, lab-oriented education (on FutureGrid)
  • Need to support appliances representing clusters of resources
  • Virtual machines + social/virtual networking to create sandboxed modules
    • Virtual “Grid” appliances: self-contained, pre-packaged execution environments
    • Group VPNs: simple management of virtual clusters by students and educators
why use virtualization
Why use Virtualization?

Traditional ways of delivering hands-on training and education in parallel/distributed computing have non-trivial dependences on the environment

Difficult to replicate same environment on different resources (e.g. HPC clusters, desktops)

Difficult to cope with changes in the environment (e.g. software upgrades)

Virtualization technologies remove key software dependences through a layer of indirection

appliance infrastructure guiding principles
Appliance Infrastructure - guiding principles

Fidelity: activities should use full-fledged, executable software: education/training modules

Learn using the proper tools

Reproducibility: Creators of content should be able to install, configure, and test their modules once, and be assured of the same functional behavior regardless of where the module is deployed

Incentive to invest effort in developing, testing and documenting new modules

appliance infrastructure guiding principles1
Appliance Infrastructure - guiding principles

Deployability: Students and users should be able to deploy modules in a simple manner, and in a variety of resources

Reduce barriers to entry; avoid dependences upon a particular infrastructure

Community-oriented: Modules should be simple to share, discover, reuse, and expand

Create conditions for “viral” growth

towards this vision in futuregrid
Towards this vision in FutureGrid

Executable modules – virtual appliances

Deployable on FutureGrid resources

Deployable on other cloud platforms, as well as virtualized desktops

Community sharing – Web 2.0 portal, appliance image repositories

An aggregation hub for executable modules and documentation

virtual appliance example
Virtual appliance example

Linux, Java, Hadoop, configuration scripts



A Hadoop worker

Another Hadoop worker






virtual cluster appliances
Virtual cluster appliances

Virtual appliance + virtual network







Another Hadoop worker

A Hadoop worker