November 2008

NCDM receives SC|08 Conference Bandwidth Challenge Award.

AUSTIN, Texas, Nov. 20 -- SC08 -- The National Center for Data Mining (NCDM) at UIC and the Open Cloud Consortium were awarded the 2008 SC08 Bandwidth Challenge award at SC08 today in Austin.

Their entry was titled "Towards Global Scale Cloud Computing: Using Sector and Sphere on the Open Cloud Testbed" and was led by Dr. Yunhong Gu of the University of Illinois at Chicago and Dr. Robert Grossman of the University of Illinois at Chicago and Open Data Group.

Although cloud computing is common today, processing data by clouds today is almost always done within a single datacenter due to the technical challenges processing data across multiple datacenters. The team today demonstrated technology for the first time that enables cloud computing to utilize high performance networks and spread cloud computing across datacenters to create wide area clouds. The technology that makes this possible is the open source Sector storage cloud and Sphere compute cloud developed by the NCDM.

NCDM used the Open Cloud Testbed, which is a testbed managed by the Open Cloud Consortium for this challenge. The Open Cloud Consortium develops standards for computing within clouds and frameworks for interoperating between clouds.

"A whole new generation of cloud computing is now possible using the open source Sector storage cloud and the Sphere computing cloud and standards developed by the Open Cloud Consortium. For the first time, developing applications that span multiple distributed clouds is now possible," according to Robert Grossman.

According to Joe Mambretti, director of the International Center of Advanced Internet Research at Northwestern University and co-director of the Open Cloud Testbed, "These innovative technologies provide unique capabilities that will enable new generations of applications based on extremely large scale data streams."

During the Bandwidth Challenge at SC08, the team demonstrated three applications that used the Sector/Sphere cloud. The application transported bioinformatics data using Sector from the conference floor in Austin to Kitakyushu in Japan at over 8 Gb/s.

The second application demonstrated was Creditstone, which is a benchmark for financial services applications. The Sector/Sphere implementation of Creditstone processed about 53.5 billion synthetic credit card transaction records in less than 1 hour.

The third application was TeraSort, which sorted 1 terabyte of data within 30 minutes. The average data moving rate was about 4.8Gb/s in the Open Cloud Testbed, with a peak speed reaching 10Gb/s.

One of the key achievements of the Sector and Sphere software is that it is very easy to use. For example, the TeraSort code only requires about 50 lines of C++ code. This is critical, as it allows researchers to use their time to focus on research problems, rather than spending time dealing with distributed programming.

According to Yunhong Gu, "Sphere is a new software system that supports simplified distributed data processing application development. In contrast to traditional distributed computing methods such as MPI, Sphere allows users to write distributed applications with a few lines of code and without knowing the details of the underlying hardware."

The NCDM is supported, in part, by the National Science Foundation, the Chicago Bioinformatics Consortium, the Department of Defense, and the University of Illinois at Chicago, as well as by other funding agenices and NCDM's industrial partners.

Center's Recent projects:

• Teraflow Testbed - distributed infrastructure designed to use new 10 GB/s network protocols and data services.
• Sector - infrastructure software providing distributed data storage, access and analysis/processing functionality.
• Angle - network monitoring software to detect anomalous network events across multiple monitoring sites.
• SidGrid - social informatics data collection and collaborative analysis software utilizing web and grid services.

The Center focuses on three research areas:

• Scaling algorithms, applications and systems to massive data sets.
• Developing algorithms, applications, and systems for mining distributed data.
• Establishing standard languages, protocols, and services for data mining and predictive modeling.

The LAC is a co-founding member of the Data Mining Group (DMG), which develops the Predictive Model Markup Language (PMML) and related standards.

Recent News and Awards page.