Reference architectures (RAs) are useful tools to understand and build complex systems, and many cloud providers and software product vendors have developed versions of them. RAs describe at an abstract level (no implementation details) the main features of their cloud systems. Security is a fundamental concern in clouds and several cloud vendors provide security reference architectures (SRAs) to describe the security features of their services. A SRA is an abstract architecture describing a conceptual model of security for a cloud system and provides a way to specify security requirements for a wide range of concrete architectures. We propose here a method to build a SRA for clouds defined using UML models and patterns, which goes beyond existing models in providing a global view and a more precise description. We present a metamodel as well as security and misuse patterns for this purpose. We validate our approach by showing that it can describe more precisely existing models and that it has a variety of uses. We describe in detail one of these uses, a way of evaluating the security level of a SRA.

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Computer grids are systems containing heterogeneous, autonomous and geographically distributed nodes. The proper functioning of a grid depends mainly on the efficient management of grid resources to carry out the various jobs that users send to the grid. This paper proposes an algorithm that uses intelligent agents in each node to perform global scheduling in a collaborative and coordinated way. The algorithm was implemented in a grid simulation environment that allows the incorporation of intelligent agents. This simulation environment was designed and developed to run and analyze the behavior of the proposed algorithm, which outperforms the numerical performance of two well-known algorithms in terms of balancing the load and making use of the grid's capacity without giving preference to any node.

Ensemble learning has gained considerable attention in different tasks including regression, classification and clustering. Adaboost and Bagging are two popular approaches used to train these models. The former provides accurate estimations in regression settings but is computationally expensive because of its inherently sequential structure, while the latter is less accurate but highly efficient. One of the drawbacks of the ensemble algorithms is the high computational cost of the training stage. To address this issue, we propose a parallel implementation of the Resampling Local Negative Correlation (RLNC) algorithm for training a neural network ensemble in order to acquire a competitive accuracy like that of Adaboost and an efficiency comparable to that of Bagging. We test our approach on both synthetic and real datasets from the UCI and Statlib repositories for the regression task. In particular, our fine-grained parallel approach allows us to achieve a satisfactory balance between accuracy and parallel efficiency.

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