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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.<\/jats:p>","DOI":"10.1145\/2479942.2479951","type":"journal-article","created":{"date-parts":[[2013,5,1]],"date-time":"2013-05-01T19:47:09Z","timestamp":1367437629000},"page":"73-82","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Modelling exogenous variability in cloud deployments"],"prefix":"10.1145","volume":"40","author":[{"given":"Giuliano","family":"Casale","sequence":"first","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirco","family":"Tribastone","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t, Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,4,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Apache Derby project. http:\/\/db.apache.org.  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