Affine Scalarization of Two-Dimensional Utility Using the Pareto Front

Cloud computing promises flexibility, and allows applications to dynamically scale or change configuration in response to demand. Autonomic deployment is the best way to manage such applications, and the deployment decisions should aim to optimize the application owner's utility. In general this leads to multi-objective deployment decisions over multiple utility dimensions. Such problems are typically managed by forming a scalar utility as a weighted combination of various objective dimensions. However, then the maximum utility is not only depending on the utility dimensions, but also on the weights used in the scalarization. This paper proposes an approach that has the potential to reduce the number of possible deployment configurations to consider, namely the ones with least sensitivity to the weights used in the scalarization and demonstrates this approach for a small industrial application for the bi-criterion case, which is of practical importance as many real Cloud deployments aim to simultaneously minimizing the deployment cost utility dimension and maximizing the application performance utility dimension.