The Open Science Data Cloud Robert L. Grossman University of Chicago and Open Cloud Consortium April 4, 2012 A 501(c)(3) not-for-profit operating clouds.

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Presentation transcript:

The Open Science Data Cloud Robert L. Grossman University of Chicago and Open Cloud Consortium April 4, 2012 A 501(c)(3) not-for-profit operating clouds for science.

2 U.S based not-for-profit corporation. Manages cloud computing infrastructure to support scientific research: Open Science Data Cloud. Manages cloud computing testbeds: Open Cloud Testbed. Develops reference implementations of standards based software for clouds

Small Medium to Large Very Large Data Size 10’s 100’s 1000’s Number Public infrastructure Dedicated infrastructure Shared community infrastructure Individual scientists & small projects Community based science via Science as a Service very large projects

Varieties of Clouds Types of clouds – Utility Clouds (e.g. Amazon) – Data Clouds (e.g. Hadoop) – Storage Clouds (e.g. Dropbox) – HPC Clouds Types of cloud service providers (CSP) – Commercial (e.g. Amazon) – Science CSP – Health related (CSP) (sign BAA) – Defense related (CSP)

I have built a VM on AWS. I have ported an app to AWS. It runs on a single VM. My cloud app scales out. I’m a 2MW boutique CSP. Boutique CSP with multiple facilities & intercloud services. My cloud app scales out & is fault tolerant. AWS, Google, FB OCC Pilot projects

Science Clouds Commercial CSP PerspectiveDemocratize access to data. Integrate data to make discoveries. Long term archive. As long as you pay the bill; as long as we keep our business model. DataData intensive computing Internet style scale out FlowsLarge data flows in and out Lots of small web flows Lock inData liberationLock in is good

New Opportunities 1.Simplify the management, analysis and sharing of data, especially big data for researchers. 2.New opportunities for discovery by integrating data at scale from within a discipline and across disciplines. 3.By thinking of a data center as an instrument, you have a new instrument for creating a new science of data.

Key Challenges Building the required infrastructure at the required scale. Developing new software tools for exploring and integrating data at scale. Brining together (either physically or virtually) enough data to change the way we make discoveries. Security, compliance & payments.

Next Steps Start some pilot projects. Create some data hubs. Create some large scale services between the data hubs. Create some open source blue prints for container based facilities.

Questions?