We introduce the Aeolus component model, which is specifically designed to capture realistic scenarii arising when configuring and deploying distributed applications in the so-called cloud environments, where interconnected components can be deployed on clusters of heterogeneous virtual machines, which can be in turn created, destroyed, and connected on-the-fly. The full Aeolus model is able to describe several component characteristics such as dependencies, conflicts, non-functional requirements (replication requests and load limits), as well as the fact that component interfaces to the world might vary depending on the internal component state.When the number of components needed to build an application grows, it becomes important to be able to automate activities such as deployment and reconfiguration. This correspond, at the level of the model, to the ability to decide whether a desired target system configuration is reachable, which we call the achievability problem, and producing a path to reach it.In this work we show that the achievability problem is undecidable for the full Aeolus model, a strong limiting result for automated configuration in the cloud. We also show that the problem becomes decidable, but Ackermannhard, as soon as one drops non-functional requirements. Finally, we provide a polynomial time algorithm for the further restriction of the model where support for inter-component conflicts is also removed.Keywords: software component, model, cloud computing, distributed systems ✩ This paper is an extended and revised version of [1, 2] -a detailed comparison with these papers is reported in the related work Section 5. This work has been developed within the ANR-2010-SEGI-013-01 project Aeolus "Mastering the Complexity of the Cloud": we would like to thank all the project partners for the numerous discussions that contributed to the definition of the Aeolus model. This work has been partially performed at IRILL, center for Free Software Research and Innovation in Paris, France,
Abstract*** To appear in Theory and Practice of Logic Programming (TPLP) *** Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to computewithout learning an explicit model -a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.
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