Guest Editorial: Cloud Computing and Scientific Applications (CCSA)—Big Data Analysis in the Cloud

Abstract

Cloud computing has been widely adopted by enterprises in their business application in the last decade. Although Cloud computing was incepted with business computing needs in mind, it is increasingly being considered by scientific communities. In recent times, a number of science Clouds have been built for specific research communities. Many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the re-enactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate Clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy Cloud, BioBlend. Whilst there has been many concerns about the capability of virtualized Cloud resources to meet the needs of scientific computation, recent results have shown that the performance of public Clouds are comparable with HPC systems for notable applications. In recent times, a combination of science Cloud and public Cloud has also been used for scientific applications. Given that a large number of scientific applications and research communities use automated process controls, usually in the form of workflows, there is a need to facilitate workflow execution on scalable platforms, such as public Clouds. Along with generic Cloud-based workflow systems, many specific Cloud-based workflow systems have been developed for science applications such as life sciences, and astronomy.