Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application redimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static thresholdbased rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

SpiNNaker is a massively parallel architecture designed to model large-scale spiking neural networks in (biological) real-time. Its design is based around ad-hoc multi-core System-on-Chips which are interconnected using a two-dimensional toroidal triangular mesh. Neurons are modeled in software and their spikes generate packets that propagate through the on-and inter-chip communication fabric relying on custom-made on-chip multicast routers. This paper models and evaluates large-scale instances of its novel interconnect (more than 65 thousand nodes, or over one million computing cores), focusing on real-time features and fault-tolerance. The key contribution can be summarized as understanding the properties of the feasible topologies and establishing the stable operation of the SpiNNaker under different levels of degradation. First we derive analytically the topological characteristics of the network, which are later confirmed by experimental work. With the computational model developed, we investigate the topology of SpiNNaker, and compare it with a standard 3-dimensional torus. The novel emergency routing mechanism, implemented within the routers, allows the topology of SpiNNaker to be more robust than the 3-dimensional torus, regardless of the latter having better topological characteristics. Furthermore, we obtain optimal values of two router parameters related with livelock and deadlock avoidance mechanisms.

High performance computing platform is moving from homogeneous individual unites to heterogeneous systems. Where each unit is a combination of homogeneous cores and accelerator devices. Accelerator such as GPUs, FPGAs, DSPs, these devices usually designed for the specific and intensive type of computing tasks. The presence of these devices have created fresh and attractive development platforms for developers and designers, brand new performance analysis frameworks and optimization tools. This is the cutting edge in the performance of some accelerator devices like GPUs and Intel's Xeon Phi. We outline some of the existing heterogeneous systems and their development frameworks. The core of this study is a review of performance modeling of these devices. In this paper, we address the emerging issues that affect the performance of these devices and associated techniques employed for simulation and evaluation.

The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. In addition, we also present the characteristics and motivations behind the use of each of these techniques and propose more adequate