Hong Kong University of Science and Technology
Cloud-based systems and the datacenter computing environment present a series of challenges to system designers for supporting massively concurrent computation on clusters with commodity hardware. The platform software should abstract the unreliable but highly provisioned hardware to provide a high-performance platform for a diversity of concurrent programs processing potentially very large data sets. Toward this goal, a number of solutions are designed or proposed. Among these products and systems, the authors elect three technologies, MapReduce/Hadoop, DVM, and Windows Azure, as representatives of three different approaches to constructing the infrastructure and instructing the programming in the cloud.