Carnegie Mellon joins Open Cirrus, continues to grow cloud research expertise

Carnegie Mellon University’s School of Computer Science announced this week that it is has joined the Open Cirrus project, adding one of its clusters to resources available on the open-source test bed for cloud computing research.

A computing cluster housed in Carnegie Mellon’s Data Center Observatory will provide additional resources for Carnegie Mellon faculty and other researchers worldwide. Open Cirrus was launched in 2008 by HP, Intel and Yahoo! to promote open collaboration among industry, academia and governments on data-intensive, Internet-scale computing. The test bed now includes cloud computing infrastructure at 10 “centers of excellence” worldwide.

…”Having a facility like this and being able to participate in Open Cirrus will provide us with unprecedented opportunities for research and education on Internet-scale computing,” said Randal E. Bryant, dean of the School of Computer Science. “We see applications well beyond those being pursued by industry today, including astronomy, neuroscience, and knowledge extraction and representation, and we will be able to delve more deeply into the design of the system itself.”

CM has been active in cloud research that has emphasized ways to make cloud computing faster and more reliable, and ways to use cloud resources for new applications.

Carnegie Mellon computer scientists have been leaders as cloud computing has emerged as a focus of academic research. Carnegie Mellon was the first university to make use of M45, a 4,000-processor, Hadoop-based computing cluster that Yahoo! made available to academic researchers beginning in late 2007. Since then, M45 research by Carnegie Mellon has resulted in infrastructure innovations, such as new approaches to diagnosing performance problems and a technique for shrinking the storage requirements for data files by 33 percent. Carnegie Mellon researchers also have used the M45 cluster to pioneer new applications that require Internet-scale resources, such as natural language processing, automated extraction of knowledge from the Web and developing a deeper understanding of when the “wisdom of crowds” is effective and when it is not for services such as Wikipedia.