In this paper, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application’ scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows us to deal with geographically distributed cloud-based applications and a large volume of computation. This can be useful in look ahead optimization and in adaptations using complex models, such as machine learning. We formally prove the safety and liveness properties of the implemented distributed algorithms. Through experiments on Amazon AWS cloud, using two different use cases, we demonstrate the elasticity and flexibility of the autonomic manager as a measure of its applicability to different cloud applications with different types of workloads. Experiments also demonstrate that increasing the size of a look ahead window, up to a certain size, improves the accuracy of the adaptation decisions up to 50%.

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So far, valuable research studies have been conducted on mapping notations of object-oriented speci cation, such as Object-Z, in di erent object-oriented programming languages, such as C++. However, the results of selecting JVM-based programming languages for mapping have not covered most of basic Object-Z structures. In this paper, the Groovy language, as a dynamic JVM-based language, is selected to overcome some of the existing limitations. As the main contribution, the rules required for mapping Object-Z speci cations to execute Groovy code are introduced. The proposed rules cover notions such as multiple inheritance, inverse speci cation of functions, functions de ned on generic de nitions, and free-type constructors. Previous methods have not covered these notions for the formal development of program from object-oriented speci cations, regardless of the selected formal speci cation language and target programming language. In addition, in this paper, the parallel composition construct is mapped to a parallel, executable code to improve the faithfulness of the nal code to the initial speci cation. A mapping rule for the class union construct is introduced, which has not yet been provided for any JVMbased language. Unlike previous works, instead of presenting the mapping rules in terms of natural languages, they are presented in terms of some formal mapping rules.