Cost minimization for computational applications on hybrid cloud infrastructures

We address the problem of task planning on multiple clouds formulated as a mixed integer nonlinear programming problem (MINLP). Its specification with AMPL modeling language allows us to apply solvers such as Bonmin and Cbc. Our model assumes multiple heterogeneous compute and storage cloud providers, such as Amazon, Rackspace, GoGrid, ElasticHosts and a private cloud, parameterized by costs and performance, including constraints on maximum number of resources at each cloud. The optimization objective is the total cost, under deadline constraint. We compute the relation between deadline and cost for a sample set of data- and compute-intensive tasks, representing bioinformatics experiments. Our results illustrate typical problems when making decisions on deployment planning on clouds and how they can be addressed using optimization techniques.

► We address the cost optimization of computational applications on hybrid clouds.
► We use mixed integer nonlinear programming and AMPL modeling language.
► We evaluate the model on limited and unlimited public and private clouds.
► We test the model on a wide range of deadline and data size parameters.
► We assess how sensitive are costs to the change of deadlines.