arXiv subjects

Carlos Guerrero

Publications and source records attributed to Carlos Guerrero.

At least 19 recordsLinked to original sources

arXiv2026

Hybrid multi-objective evolutionary algorithms for service placement in the computing continuum: a comparative study with genetic traceability

This paper addresses multi-objective service placement in computing continuum environments through a collaborative hybrid island-model MOEA. The key innovation is not the design of a new general hybrid algorithm, but the systematic application and analysis of heterogeneous hybridization for this specific optimization domain through two independent experimental campaigns: a first one with four state-of-the-art MOEAs (NSGA-II, NSGA-III, U-NSGA-III, and SMS-EMOA), and a second one with a complementary hybrid configuration based on NSGA-II, MOEA/TS, and MOCPO, both co-evolving and periodically exchanging solutions. These designs enable complementary search behaviors across islands and are naturally aligned with the distributed edge-fog-cloud architecture of the computing continuum, facilitating scalable parallel execution. To evaluate the approach, we define two research hypotheses: (i) whether hybrid cooperation yields significant performance gains over standalone algorithms, and (ii) whether all constituent algorithms contribute equally to the final outcomes. We combine standard Pareto-front quality indicators (GD, IGD, HV, S, and STE) with a traceability-oriented analysis based on genetic load, which quantifies the contribution of each island to the evolved solutions. Across 30 independent runs, the hybrid method outperforms most of the standalone baselines, and statistical tests confirm significant improvements. Results also show non-uniform contributions among islands, providing interpretable evidence of effective hybrid cooperation.

cs.NE↗

arXiv2026

Multi-objective application placement in fog computing using graph neural network-based reinforcement learning

We propose a framework designed to tackle a multi-objective optimization challenge related to the placement of applications in fog computing, employing a deep reinforcement learning (DRL) approach. Unlike other optimization techniques, such as integer linear programming or genetic algorithms, DRL models are applied in real time to solve similar problem situations after training. Our model comprises a learning process featuring a graph neural network and two actor-critics, providing a holistic perspective on the priorities concerning interconnected services that constitute an application. The learning model incorporates the relationships between services as a crucial factor in placement decisions: Services with higher dependencies take precedence in location selection. Our experimental investigation involves illustrative cases where we compare our results with baseline strategies and genetic algorithms. We observed a comparable Pareto set with negligible execution times, measured in the order of milliseconds, in contrast to the hours required by alternative approaches.

cs.DC↗

arXiv2026

Analysis of wireless network access logs for a hierarchical characterization of user mobility

This paper presents a method that generates a hierarchical user mobility model from the analysis of the data available from Wi-Fi connections. The data obtained from the Wi-Fi infrastructure is defined in terms of the coverage areas of the access points that the users move through. These access points are recursively grouped into different levels of granularity based on their geospatial features. The track of a user is defined as a sequence of Wi-Fi access points, which is enough to simulate user mobility in, for example, fog scenarios. The hierarchical definition of the region under study is proposed to reduce the complexity of the model in high-scale scenarios and to increase the adaptability between scenarios with different geospatial features. The model creation is based on a user profiling method that uses a clustering algorithm and each user type is defined with a transition matrix between coverage areas and a time length vector for the areas. The method is applied to the case of the campus of the University of the Balearic Islands. From the analysis of the mean square error of the results, we determined that the proposed method obtains good results for the transition matrices, but that the time vector definition should be improved. The results also show lower complexity in the case of the hierarchical model, with one area for each building and three levels, in regard to a non-hierarchical model, with only one area and one level for the whole campus.

cs.DC↗

arXiv2026

YAIFS: Yet (not) Another Intelligent Fog Simulator: A Framework for Agent-Driven Computing Continuum Modeling & Simulation

Simulation plays a key role in the design and evaluation of distributed systems, yet it is often treated as a static tool with limited interaction capabilities. In this work, we present Yet (not) Another Intelligent Fog Simulator (YAIFS), and evolution of YAFS that redefines simulation as an interactive, service-oriented environment. YAIFS introduce a layered architecture that exposes the simulation through a unified API and service interface, enabling external entities to observe, control, and modify its execution. A central contribution is the integration of the Model Context Protocol (MCP) as a standardized interaction layer between agents and the simulation. Through MCP, heterogeneous agents can access state, invoke actions and coordinate behavior using a common set of tools, decoupling agent experimentation workflows. We illustrate these capabilities through two scenarios: an LLM-based assistant that enable natural language control of simulations, and a multi-agent setting where agents monitor system conditions and adapt placement decisions at runtime. These scenarios demonstrate how MCP structures agent-simulation interaction and enable adaptive behavior under dynamic workloads. The proposed approach transforms simulation into an interactive and programmable environment, opening new directions for AI-driven experimentation in cloud-edge systems. The implementation is publicly available at: http://github.com/acsicuib/YAIFS

cs.DC↗

arXiv2025

Design and commissioning of the miniBELEN neutron counter for the study of $(α,n)$ reactions

The miniBELEN detector is a moderated neutron counter based on the use of $^3$He tubes and high-density polyethylene as moderator. The detector has been designed to have a neutron detection efficiency that is nearly independent from the initial neutron energy for ($α$,n) reactions with alpha-particle energies up to 15 MeV. In order to achieve that, an innovative design methodology based on the use of cadmium neutron filters has been used. Monte Carlo calculations of the detector response have been validated using a $^{252}$Cf fission source. Measurements of the relatively well-known ($α$,n) thick-target yields on aluminum have been found to be consistent with data from previous works. A method has been developed to correlate the counting rate ring ratios with the mean neutron energy and validated using previous time-of-flight measurements of the aluminum ($α$,n) spectra and with a $^{252}$Cf fission source. Submitted to The European Physical Journal - Plus

physics.ins-det↗

arXiv2025

Measurement of the $^{35}Cl(n, p)^{35}S$ cross-section at the CERN n\_TOF facility from subthermal energy to 120 keV

Background: The $^{35}Cl(n, p)^{35}S$ reaction is of special interest in three different applications. First, in Boron Neutron Capture Therapy due to the presence of $^{35}Cl$ in brain and skin tissue. Second, it is involved in the creation of $^{36}S$, whose astrophysical origin remains unresolved. Third, in the designing of fast nuclear reactors of new generation based on molten salts. Purpose: To measure the $^{35}Cl(n, p)^{35}S$ cross-section from thermal energy to 120 keV, determine the resonance parameters in this range and Maxwellian Averaged Cross-Section (MACS). Method: We made use of the Time-of-Flight technique with microMEGAS detectors at Experimental Area 2 (EAR-2) of n\_TOF facility at CERN. The $^{10}B(n, α)^{7}Li$ and $^{235}U(n, f)$ reactions were used as references. Rutherford Back-scattering Spectrometry technique was performed at Centro Nacional de Aceleradores (CNA) in Sevilla, in order to accurately determine the masses of the irradiated samples. Results: We obtain a thermal cross-section of $0.470 \pm 0.009$ barns. The $1/v$ energy dependence of the cross-section is observed up to the first resonance at 0.398 keV, the resonances up to 120 keV are analyzed and MACS calculated for $k_{B} T$ from 1 to 100 keV. Conclusions: The $^{35}Cl(n, p)^{35}S$ cross-section has been obtained over a wide energy range for the first time, with high accuracy across the aforementioned range. The thermal cross-section and first two resonances are in agreement with latest evaluation in ENDF/B-VIII.1, while lower resonance strength was found for high energy resonances. These data are used to calculate the MACS for different $k_{B} T$.

nucl-ex↗

arXiv2025

Enhancing Neutron Measurement Accuracy with Bubble Detectors at Laser-Driven Neutron Sources

Bubble detectors are widely used to measure neutron flux from laser-driven sources employing a pitcher-catcher setup, due to their insensitivity to intense $γ$-ray backgrounds and strong electromagnetic pulses (EMP).\\ This paper presents a method to account for the neutron energy-dependent response of bubble detectors, enabling accurate conversion of bubble counts into neutron flux at the detector location. The proposed method is based on the accurate reconstruction of the response function using a surrogate model. The resulting model is convoluted with the (normalized) expected/measured neutron spectrum to obtain an effective measure of the bubble detector's response, herein referred to as effective $c$ or $c_\text{eff}$. This effective value for the response is energy-independent after the convolution. In this way, our approach includes the spectral distribution of neutrons arriving at the detector to determine the integral neutron flux. Analyzing our experimental results obtained at the DRACO PW laser and comparing the results to previously used methods to obtain neutron fluxes from bubble detectors returns a reduction in neutron flux of up to \SI{31}{\%}. Results from the method detailed in this paper agree with in-depth experimental setup Monte Carlo simulations, with deviations of less than \SI{10}{\%}. We furthermore discuss the inherent limitations of our method with regard to its uncertainty and highlight the influence of neutron scattering in bubble detector measurements. For our experimental setup at the DRACO laser, up to \SI{47}{\%} of the detected neutrons arrive at the detector after undergoing at least one scattering event.

physics.ins-det↗

arXiv2025

Hybrid Compton-PET Imaging for ion-range verification:A Preclinical Study for Proton-, Helium-, and Carbon-Therapy at HIT

Enhanced-accuracy ion-range verification in real time shall enable a significant step forward in the use of therapeutic ion beams. Positron-emission tomography (PET) and prompt-gamma imaging (PGI) are two of the most promising and researched methodologies, both of them with their own advantages and challenges. Thus far, both of them have been explored for ion-range verification in an independent way. However, the simultaneous combination of PET and PGI within the same imaging framework may open-up the possibility to exploit more efficiently all radiative emissions excited in the tissue by the ion beam. Here we report on the first pre-clinical implementation of an hybrid PET-PGI imaging system, hereby exploring its performance over several ion-beam species (H, He and C), energies (55 MeV to 275 MeV) and intensities (10$^7$-10$^9$ ions/spot), which are representative of clinical conditions. The measurements were carried out using the pencil-beam scanning technique at the synchrotron accelerator of the Heavy Ion Therapy centre in Heidelberg utilizing an array of four Compton cameras in a twofold front-to-front configuration. The results demonstrate that the hybrid PET-PGI technique can be well suited for relatively low energies (55-155 MeV) and beams of protons. On the other hand, for heavier beams of helium and carbon ions at higher energies (155-275 MeV), range monitoring becomes more challenging owing to large backgrounds from additional nuclear processes. The experimental results are well understood on the basis of realistic Monte Carlo (MC) calculations, which show a satisfactory agreement with the measured data. This work can guide further upgrades of the hybrid PET-PGI system towards a clinical implementation of this innovative technique.

physics.med-ph↗

arXiv2025

Declarative Application Management in the Fog. A bacteria-inspired decentralised approach

Orchestrating next gen applications over hterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of bacteria colonies, we propose a declarative, fully decentralised application management solution, targeting pervasive opportunistic Cloud-IoT infrastructures. We present acustomisable declarative implementation of the approach and validate its scalability through simulation over motivating scenarios, also considering end-user's mobility and the possibility to enforce application-specific management policies for different classes of applications.

cs.DC↗

arXiv2025

Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures

This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.

cs.NE↗

arXiv2024

Use of social networks to motivate computer-engineering students to participate in self-assessment activities

Motivation is essential in the learning process of university students, and teachers should have a wide range of strategies to address this issue. The emergence of social technologies has had a considerable influence in e-learning systems, and a number of experts state that their use is a good method to motivate students and to increase their participation in activities. This study attempts to determine whether social networks and social applications should be viewed as many other tools or whether they can actually provide extra motivation for students to participate. The study compared the percentage of student participation in tasks of self-assessment. The experiments covered three traditional strategies of student motivation and another one in which social networks were used to introduce, explain and deliver the self-assessment tasks. The case with a higher participation was the one in which students obtained a reward from the completion of the activity. Despite this result, the statistical analysis indicated that the use of social networks obtained similar results as a strategy of continuous and regular motivational speeches.

cs.CY↗

arXiv2024

Use of Mobile Devices in the Classroom to Increase Motivation and Participation of Engineering University Students

The aim of this study was to see whether student participation increased when mobile devices were used in the classroom. We measured the amount of student participative actions when the Socrative tool was used and when it was not used. Our experiment involved a total of 192 students, corresponding to 4 different subjects of Computer Engineering at the Universitat de les Illes Balears, during 2012/2013 and 2013/2014 courses. An independent paired t-test was performed on the measurements. The analysis results show that student participation increases with the use of mobile devices for theory classes and students are willing to participate in class activities and share their own results.

cs.CY↗

arXiv2024

A platform for lightweight deployment of IoT applications based on a Function-as-a-Service model

This paper presents a platform to facilitate the deployment of applications in Internet of Things (IoT) devices. The platform allows to the programmers to use a Function-as-a-Service programming paradigm that are managed and configured in a Platform-as-a-Service web tool. The tool also allows to establish interoperability between the functions of the applications. The proposed platform obtained faster and easier deployments of the applications and the resource usages of the IoT devices also were lower in relation to a deployment process based in containers of Docker.

cs.DC↗

arXiv2024

Optimization policy for file replica placement in fog domains

Fog computing architectures distribute computational and storage resources along the continuum from the cloud to things. Therefore, the execution of services or the storage of files can be closer to the users. The main objectives of fog computing domains are to reduce the user latency and the network usage. Availability is also an issue in fog architectures because the topology of the network does not guarantee redundant links between devices. Consequently, the definition of placement polices is a key challenge. We propose a placement policy for data replication to increase data availability that contrasts with other storage policies that only consider a single replica of the files. The system is modeled with complex weighted networks and topological features, such as centrality indices. Graph partition algorithms are evaluated to select the fog devices that store data replicas. Our approach is compared with two other placement policies: one that stores only one replica and FogStore, which also stores file replicas but uses a greedy approach (the shortest path). We analyze 22 experiments with simulations. The results show that our approach obtains the shortest latency times, mainly for writing operations, a smaller network usage increase, and a similar file availability to FogStore.

cs.DC↗

arXiv2024

Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

The increasing complexity of fog computing environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization in fog computing, within an increasing degree of distribution. The designs leverage the execution of the GA in the fog devices themselves by dealing with the specific features of this domain: constrained resources and widely geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the fog service placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared with a control case, a traditional centralized version of this GA algorithm, considering solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. Finally, the proposal with a distributed population and that only interchanges solution between the workers' neighbors achieves the lowest network load but with compromised solution quality.

cs.NE↗

arXiv2024

Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models

Reference Evapotranspiration (ET0) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET0 computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute ET0 using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET0 estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET0 estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors' previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET0 estimation by an ANN model, and (ii) estimate SR by ANN model, and then use that estimation for ET0 computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R2) ranging between 0.893 and 0.667, when considering forecasts up to 15 days.

cs.AI↗

arXiv2024

Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture

The use of containers in cloud architectures has become widespread because of advantages such as limited overhead, easier and faster deployment and higher portability. Moreover, they are a suitable architectural solution for deployment of applications created using a microservices development pattern. Despite the large number of solutions and implementations, open issues have not been addressed in container automation and management. Container resource allocation influences system performance and resource consumption so it is a key factor for cloud providers. We propose a genetic algorithm approach, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), to optimize container allocation and elasticity management due to the good results obtained with this algorithm in other resource management optimization problems in cloud architectures. The optimization has been focused on a tight use of the resources and a reduction of the network overhead and system failure rate. A model for cloud cluster, containers, microservices and four optimization objectives is presented. Experimental results have shown that our approach is a suitable solution to address the problem of container allocation and elasticity and it obtains better objectives values than the container management policies implemented in Kubernetes.

cs.NI↗

arXiv2024

A lightweight decentralized service placement policy for performance optimization in fog computing

A decentralized optimization policy for service placement in fog computing is presented. The optimization is addressed to place most popular services as closer to the users as possible. The experimental validation is done in the iFogSim simulator and by comparing our algorithm with the simulator's built-in policy. The simulation is characterized by modeling a microservice-based application for different experiment sizes. Results showed that our decentralized algorithm places most popular services closer to users, improving network usage and service latency of the most requested applications, at the expense of a latency increment for the less requested services and a greater number of service migrations.

cs.NI↗