Clusters, Grids and Clouds: A Look from Both Sides

Summary form only given. In science and engineering, a tsunami of new experimental and computational data and a suite of increasingly ubiquitous sensors pose vexing problems in data analysis, transport, visualization and collaboration. Cloud computing and "big data," together with our experiences with clusters and grids, are extending our notions of computational science and engineering, bringing technical, political and economic challenges. What are the software structures and capabilities that best exploit these capabilities and economics while providing application compatibility and community continuity? What are the appropriate roles of public clouds relative to local computing systems, private clouds and grids? How can we best exploit elasticity for peak demand? How do we optimize performance and reliability? How do we provide privacy and security? How do we balance traditional HPC investments against distributed systems and big data opportunities and avoid past research infrastructure pitfalls? How do we integrate the emerging Internet of Things and ubiquitous sensors for multidisciplinary fusion, while also managing security and privacy?