Automated deployment of component-based applications in the Cloud consists in the allocation of virtual machines (VMs) offers from various Cloud Providers such that the constraints induced by the interactions between ...
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Automated deployment of component-based applications in the Cloud consists in the allocation of virtual machines (VMs) offers from various Cloud Providers such that the constraints induced by the interactions between components and by the components hardware/software requirements are satisfied and the performance objectives are optimized (e.g. costs are minimized). It can be formulated as a constraint optimization problem, hence, in principle, the optimization can be carried out automatically. In the case the set of VM offers is large (several hundreds), the computational requirement is huge, making the automatic optimization practically impossible with the current general optimization modulo theory (OMT) and mathematical programming (MP) tools. We overcame the difficulty by methodologically analyzing the particularities of the problem with the aim of identifying search space reduction methods. These are methods exploiting: (i) the symmetries of the general Cloud deployment problem, (ii) the graph representation associated to the structural constraints specific to each particular application, and (iii) their combination. An extensive experimental analysis has been conducted on four classes of real-world problems, using six symmetry breaking strategies and two types of optimization solvers. As a result, the combination of a variable reduction strategy with a column-wise symmetry breaker leads to a scalable deployment solution, when OMT is used to solve the resulting optimization problem. (C) 2021 Elsevier Inc. All rights reserved.
Modeling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting...
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Modeling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model these kinds of domains is crucial in "learning to design" tasks, that is, learning applications where the goal is to learn from examples how to perform automatic de novo design of novel objects. In this paper we present Structured Learning modulo Theories, a max-margin approach for learning in hybrid domains based on Satisfiability modulo Theories, which allows to combine Boolean reasoning and optimization over continuous linear arithmetical constraints. The main idea is to leverage a state-of-the-art generalized Satisfiability modulotheory solver for implementing the inference and separation oracles of Structured Output SVMs. We validate our method on artificial and real world scenarios. (C) 2015 Elsevier B.V. All rights reserved.
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