@article{eprints2604,
         journal = {Performance Evaluation Review},
          author = {Giuliano Casale and Mirco Tribastone},
            year = {2013},
           title = {Modelling exogenous variability in cloud deployments},
          volume = {40},
          number = {4},
           month = {March},
       publisher = {ACM},
           pages = {73--82},
             url = {http://eprints.imtlucca.it/2604/},
        abstract = {Describing exogenous variability in the resources used by a cloud application leads to stochastic performance models that are difficult to solve. In this paper, we describe the blending algorithm, a novel approximation for queueing network models immersed in a random environment. Random environments are Markov chain-based descriptions of timevarying operational conditions that evolve independently of the system state, therefore they are natural descriptors for exogenous variability in a cloud deployment. The algorithm adopts the principle of solving a separate transient-analysis subproblem for each state of the random environment. Each subproblem is then approximated by a system of ordinary differential equations formulated according to a fluid limit theorem, making the approach scalable and computationally inexpensive. A validation study on several hundred models shows that blending can save up to two orders of magnitude of computational time compared to simulation, enabling efficient exploration of a decision space, which is useful in particular at design-time.}
}