Distributed software environments are increasingly complex and dicult to manage, as they integrate various legacy software with specic management interfaces. Moreover, the fact that management tasks are performed by humans leads to many conguration errors and low reactivity. This is particularly true in medium or large-scale distributed infrastructures.To address this issue, we explore the design and implementation of an autonomic management system. The main principle is to wrap legacy software pieces in components in order to administrate a software infrastructure as a component architecture. However, we observed that the interfaces of a component model are too low-level and dicult to use. Therefore, we introduced higher-level formalisms for the specication of deployment and management policies. This paper overviews these specication facilities that are provided in the Tune autonomic management system.

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International audienceVirtualized cloud infrastructures are very popular as they allow resource mutualization and therefore cost reduction. For cloud providers, minimizing the number of used resources is one of the main services that such environments must ensure. Cloud customers are also concerned with the minimization of used resources in the cloud since they want to reduce their invoice. Thus, resource management in the cloud should be considered by the cloud provider at the virtualization level and by the cloud customers at the application level. Many research works investigate resource management strategies in these two levels. Most of them study virtual machine consolidation (according to the virtualized infrastructure utilization rate) at the virtualized level and dynamic application sizing (according to its workload) at the application level. However, these strategies are studied separately. In this article, we show that virtual machine consolidation and dynamic application sizing are complementary. We show the efficiency of the combination of these two strategies, in reducing resource usage and keeping an application’s Quality of Service. Our demonstration is done by comparing the evaluation of three resource management strategies (implemented at the virtualization level only, at the application level only, or complementary at both levels) in a private cloud infrastructure, hosting typical JEE web applications (evaluated with the RUBiS benchmark)