Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing

In the last years, one of the fields of artificial intelligence that has been investigated the most is nature-inspired computing. The research done on this specific topic showcases the interest that sparks in researchers and practitioners, who put their focus on this paradigm because of the adaptability and ability of nature-inspired algorithms to reach high-quality outcomes on a wide range of problems. In fact, this kind of methods has been successfully applied to solve real-world problems in heterogeneous fields such as medicine, transportation, industry, or software engineering. Our main objective with this paper is to describe a tool based on nature-inspired computing for solving a specific software engineering problem. The problem faced consists of optimizing Infrastructure as Code deployment configurations. For this reason, the name of the system is IaC Optimizer Platform. A prototypical version of the IOP was described in previous works, in which the functionality of this platform was introduced. With this paper, we take a step forward by describing the final release of the IOP, highlighting its main contribution regarding the current state-of-the-art, and justifying the decisions made on its implementation. Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use. To do that, we also present and solve a real-world use case.Comment: 10 pages, 5 figures, paper accepted for being presented in the upcoming 6th International Conference on Computational Intelligence and Intelligent Systems (CIIS 2023

Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations

Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. This paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. The system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. The main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. This is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 European project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs.Comment: 9 pages, 1 figure, 4 tables. Paper presented in the 11th International Conference on Computer and Communications Management (ICCCM 2023

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.This research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162)

Lamb wave-based damage indicator for plate-like structures

Structural health monitoring based on ultrasonics typically involves complex data analysis. Ultrasound monitoring based on Lamb waves techniques are extensively used nowadays due to their efficiency in exploring large areas with relatively small attenuation. In recent years, baseline based methods have been developed to identify structural damage based on the mismatch between the measured signal and the baseline one. To this end, complex time-frequency transformations are required to obtain signal features such as the time of arrival or the energy content, as indicators of damage onset and growth. Notwithstanding this, on-board applications require highly efficient processing techniques due to information storage and exchange limitations. This paper proposes a very high efficiency signal processing methodology to obtain a novel cumulative damage factor using Lamb wave raw data. The new methodology has been tested using ultrasonic and damage data from a fatigue test in carbon-epoxy composite laminates. The data is taken from NASA Prognostics data repository. In view of the results, the method is able to efficiently detect the onset and extent of damage from early stages of degradation. Moreover, the results demonstrate a remarkable agreement between the growth of delamination area and the predicted cumulative damage factor

Hacia una educación más inclusiva del alumnado con discapacidad en el área de educación física

En este artículo se presenta una apuesta por la educación inclusiva en el área de Educación Física (EF), poniendo especial interés en el colectivo de alumnado con discapacidad. Alumnado que históricamente ha sido excluido de la educación ordinaria. Sin embargo, en la actualidad, dicho colectivo está presente en los centros y aulas ordinarias y es tarea del profesorado, en este caso del de EF, ofrecer una respuesta adaptada a sus necesidades creando entornos inclusivos. Por ello, tras una revisión de estudios científicos realizados en este ámbito, se analizan barreras para el aprendizaje y la participación; y se proponen estrategias para favorecer la inclusión educativa del alumnado con discapacidad en el área de EF

Ultrasonic guided wave testing on cross-ply composite laminate:An empirical study

Structural health monitoring comprises a set of techniques to detect defects appearing in structures. One of the most viable techniques is based on the guided ultrasonic wave test (UGWT), which consists of emitting waves throughout the structure, acquiring the emitted waves with various sensors, and processing the waves to detect changes in the structure. The UGWT of layered composite structures is challenging due to the anisotropic wave propagation characteristics of such structures and to the high signal attenuation that the waves experience. Hence, very low amplitude signals that are hard to distinguish from noise are typically recovered. This paper analyzes the propagation of guided waves along a cross-ply composite laminate following an empirical methodology. The research compares several implementations for UGWT with piezoelectric wafer active sensors. The reference for comparison is set on a basic mode, which considers the application of nominal voltage to a single sensor. The attenuation and spreading of the waves in several directions are compared when more energy is applied to the monitored structure. In addition, delayed multiple emission is also considered in multisensor tests. The goal of all the UGWT configurations is to transmit more energy to the structure such that the echoes of the emission are of greater amplitude and they ease the signal processing. The study is focused on the realization of viable monitoring systems for aeronautical composite made structures.This paper is part of the SAFE-FLY project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721455. In addition, this work has been supported by a continuous collaboration between Aernnova Engineering Division S.A. and the University of the Basque Country

An Evolutionary Computation-Based Platform for Optimizing Infrastructure-as-Code Deployment Configurations

Publisher Copyright: © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.PIACERE is an H2020 European project which objective is to implement a solution involving the development, deployment, and operation of Infrastructure-as-Code of applications running on cloud continuum. This technical paper is focused on describing a specific module of the whole PIACERE ecosystem: the IaC Optimizer Platform. The main objective of this component is to provide the user with optimized Infrastructure-as-Code configurations deployed on the most appropriate infrastructural elements that best meet the predefined requirements. For properly dealing with this problem, the IaC Optimizer Platform is based on Evolutionary Computation metaheuristics. More specifically, it resorts to NSGA-II and NSGA-III algorithms, depending on user needs. Additionally, we not only describe the IaC Optimizer Platform component in this paper, but we also show how it helps the user to find the most adequate Infrastructure-as-Code configurations.This research was funded by the European project PIACERE (Horizon 2020 Program, under grant agreement no 101000162).Peer reviewe

Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing

<p><i>The main objective of this paper is to demonstrate a tool based on nature-inspired computing for solving a specific software engineering problem. In particular, the problem that has been faced consists of optimizing Infrastructure as Code (IaC) deployment configurations. The IaC Optimizer Platform (IOP) is contextualized within the complete platform in which it is embedded, describing how a user can benefit from its use. Also, a real-world use case is presented and solved.</i> </p&gt

Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing

Publisher Copyright: © 2023 ACM.In the last years, one of the fields of artificial intelligence that has been investigated the most is nature-inspired computing. The research done on this specific topic showcases the interest that sparks in researchers and practitioners, who put their focus on this paradigm because of the adaptability and ability of nature-inspired algorithms to reach high-quality outcomes on a wide range of problems. In fact, this kind of methods has been successfully applied to solve real-world problems in heterogeneous fields such as medicine, transportation, industry, or software engineering. Our main objective with this paper is to describe a tool based on nature-inspired computing for solving a specific software engineering problem. The problem faced consists of optimizing Infrastructure as Code deployment configurations. For this reason, the name of the system is IaC Optimizer Platform. A prototypical version of the IOP was described in previous works, in which the functionality of this platform was introduced. With this paper, we take a step forward by describing the final release of the IOP, highlighting its main contribution regarding the current state-of-the-art, and justifying the decisions made on its implementation. Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use. To do that, we also present and solve a real-world use case.Peer reviewe