oa Qloud: a cloud computing infrastructure for scientific applications

Cloud computing is a disruptive technology that is rapidly changing how organizations use and interact with information technology. By transforming computing infrastructure from a product to a service, it offers many benefits, including scalability of resources, flexibility for users in terms of software and hardware needs, increased reliability, decreased downtime, increased hardware utilization and reduced upfront costs and carbon footprint. Academia and research organizations are now actively involved in bringing some of those benefits to high performance and scientific computing. The Qatar Cloud Computing Center – Qloud research initiative brings Carnegie Mellon, Texas A&M, and Qatar University together to explore cloud computing to further research and development of cloud computing in Qatar and exploit it for regionally relevant scientific applications.

In partnership with IBM, two pilot cloud systems have been put in place, one on the CMUQ campus in early 2009, and another on the QU campus in 2010. These systems are available for educational and experimental use for researchers, students and faculty in Qatar. Further, an introductory course in cloud computing was held in the spring 2010 semester to equip computer science students with necessary skills to work with this new computing paradigm.

The Qloud research focuses on porting scientific applications to the cloud. Large-scale data-intensive applications can reap the benefits of cloud computing and programming models such as MapReduce. However, there is a lack of understanding of the performance implications of executing scientific applications in cloud environments, which is an impediment to increased adoption of cloud computing for these purposes. In our research, we explore the performance and behavior of various classes of scientific applications in a cloud computing environment. Specifically, we are studying the effect of provisioning variation, a variation in the performance of an application caused by the variation of resource allocation in a cloud computing environment. Our initial findings indicate that for certain application types, we observe a five-fold variation in performance between a best-case and worst-case resource mapping in our private cloud environment. This research can help in building new frameworks to support scientific computation on the cloud.