The Computer Physics Communications journal has just made available online our latest work on SaaS+PaaS architectures for service-driven computing. This is again the result of our collaboration with the Institute of Computing Technology from the Chinese Academy of Sciences and it can be accessed here.
Markov Chain Monte Carlo (MCMC) methods are widely used in the field of simulation and modelling of materials, producing applications that require a great amount of computational resources. Cloud computing represents a seamless source for these resources in the form of HPC. However, resource over-consumption can be an important drawback, specially if the cloud provision process is not appropriately optimized. In the present contribution we propose a two-level solution that, on one hand, takes advantage of approximate computing for reducing the resource demand and on the other, uses admission control policies for guaranteeing an optimal provision to running applications.