Catálogo de publicaciones - revistas

<jats:p>Cloud infrastructures are highly favoured as a computing delivery model worldwide, creating a strong societal dependence. It is therefore vital to enhance their resilience, providing persistent service delivery under a variety of conditions. Cloud environments are highly complex and continuously evolving. Additionally, the plethora of use-cases ensures requirements for persistent service delivery vary. As a contribution to knowledge, this work surveys resilience techniques for cloud environments. We apply a novel perspective using a layered model of traditional and emerging cloud paradigms. Works are then classified according to the Resilinets model. For each layer, the most common techniques with limitations are derived including an actor’s strength in influencing resilience in the cloud with each technique. We conclude with some future challenges to the field of resilient cloud computing.</jats:p>

<jats:p>Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors. In this article, we present and examine these characteristics, current solutions, as well as open challenges and future research directions in identifying new outlier detection strategies. We propose a taxonomy of the recently designed outlier detection strategies while underlying their fundamental characteristics and properties. We also introduce several newly trending outlier detection methods designed for high-dimensional data, data streams, big data, and minimally labeled data. Last, we review their advantages and limitations and then discuss future and new challenging issues.</jats:p>

<jats:p> Blockchain technology has gained tremendous popularity both in practice and academia. The goal of this article is to develop a coherent overview of the state of the art in blockchain technology, using a <jats:italic>systematic</jats:italic> (i.e., protocol-based, replicable), <jats:italic>multivocal</jats:italic> (i.e., featuring both white and grey literature alike) literature review to (1) define blockchain technology, (2) elaborate on its architecture options and (3) tradeoffs, as well as to understand (4) the current applications and challenges, as evident from the state of the art. We derive a systematic definition of blockchain technology, based on a formal concept analysis. Further, we flesh out an overview of blockchain technology elaborated by means of Grounded-Theory. </jats:p>

<jats:p>The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.</jats:p>

<jats:p>Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such perturbations, called adversarial examples, are intentionally designed to test the network’s sensitivity to distribution drifts. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. In this article, we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.</jats:p>

<jats:p>This article surveys the linear temporal logic (LTL) literature and presents all the LTL theorems from the survey, plus many new ones, in a calculational deductive system. Calculational deductive systems, developed by Dijkstra and Scholten and extended by Gries and Schneider, are based on only four inference rules—Substitution, Leibniz, Equanimity, and Transitivity. Inference rules in the older Hilbert-style systems, notably modus ponens, appear as theorems in this calculational deductive system. This article extends the calculational deductive system of Gries and Schneider to LTL, using only the same four inference rules. Although space limitations preclude giving a proof of every theorem in this article, every theorem has been proved with calculational logic.</jats:p>

<jats:p>Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation, and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.</jats:p>

<jats:p>When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, system software, and programming language compilers or their runtime systems can trade deviations from correct behavior for lower resource usage. We present, for the first time, a synthesis of research results on computing systems that only make as many errors as their end-to-end applications can tolerate. The results span the disciplines of computer-aided design of circuits, digital system design, computer architecture, programming languages, operating systems, and information theory. Rather than over-provisioning the resources controlled by each of these layers of abstraction to avoid errors, it can be more efficient to exploit the masking of errors occurring at one layer and thereby prevent those errors from propagating to a higher layer.</jats:p> <jats:p>We demonstrate the potential benefits of end-to-end approaches using two illustrative examples. We introduce a formalization of terminology that allows us to present a coherent view across the techniques traditionally used by different research communities in their individual layer of focus. Using this formalization, we survey tradeoffs for individual layers of computing systems at the circuit, architecture, operating system, and programming language levels as well as fundamental information-theoretic limits to tradeoffs between resource usage and correctness.</jats:p>

<jats:p>Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research.</jats:p>

<jats:p>Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.</jats:p>