Emergency Departments (EDs) are among the most complex areas in healthcare, requiring immediate medical attention for acute and urgent conditions. Optimizing staff configurations to reduce patient Length of Stay (LoS) and improve operational efficiency poses a significant challenge due to the combinatorial and high-dimensional nature of the problem. To identify the most effective staff configuration, we propose a heuristic optimization strategy that is based on the Montecarlo Clustering Search Algorithm (MCSA), which efficiently explores the multidimensional solution space. MCSA leverages an agent-based simulation (ABM) model that evaluates each proposed staff configuration under realistic operational conditions, providing Key Performance Indicator (KPI) feedback values related to each proposed staff configuration. Through this strategy, we explore staff configurations capable of handling patient volumes with varying acuity levels in an ED to optimize the LoS KPI. Results demonstrate that our methodology is capable to find a solution as a staff configuration that reduces LoS compared to a baseline, offering a computationally efficient and practical tool for decision-makers. We identified solutions by exploring less than 1% of the total search space, demonstrating the efficiency of the proposed approach in addressing complex optimization problems. This approach supports informed planning in healthcare environments while maintaining system feasibility and scalability.

Service-Oriented Architecture (SOA) is a key paradigm for designing scalable, modular, and efficient cloud solutions. As cloud computing adoption grows, training professionals in cloud service architectures is essential. This paper presents an innovative educational approach that fully covers the learning objectives of a cloud computing course:—Introduction to Cloud Service Architecture, Storage Services, Compute Services, Database Services, and Architectural Case Studies while leveraging cloud computing such as Amazon Web Services (AWS) and Generative Artificial Intelligence (AI) to improve learning outcomes. By integrating Generative AI tools –such as ChatGPT, Gemini, Perplexity, and Copilot - students generate, validate and analyze custom cloud computing case studies, making the learning process more interactive, adaptive, and aligned with industry standards. This approach ensures that students develop both theoretical knowledge and practical expertise, enabling them to design and deploy real-world cloud architectures while acquiring the skills to become certified as AWS Solution Architects. In addition, it improves student motivation and accelerates their transition to professional cloud roles by making learning more dynamic and efficient.

Emergency Departments (EDs) are critical yet complex areas in healthcare, where optimizing staff configurations is essential to reduce patient waiting times and improve efficiency. This study presents a methodology that combines Agent-Based Simulation (ABS) with the Montecarlo Clustering Search Algorithm (MCSA) to address the combinatorial challenge of allocating medical staff-doctors, nurses, and technicians-based on patient arrival patterns and acuity levels. The optimization process is guided by Key Performance Indicators (KPIs), particularly patient Length of Stay (LoS), ensuring staff allocations align with real-time demand without affecting resources in other hospital areas. The integrated approach enables realistic modeling of ED operations while efficiently exploring large solution spaces. Unlike exhaustive methods, MCSA significantly reduces computational time, making it suitable for practical implementation in healthcare settings. The results show that the proposed framework can effectively identify staff configurations that enhance ED performance and support scalable, data-driven decision-making. This contributes to improved patient outcomes and operational balance in high-pressure environments.

Deep learning applications have become crucially important for the analysis and prediction of massive volumes of data. However, these applications impose substantial input/output (I/O) loads on computing systems. Specifically, when running on distributed memory systems, they manage large amounts of data that must be accessed from parallel file systems during the training stage using the available I/O software stack. These accesses are inherently intensive and highly concurrent, which can saturate systems and adversely impact application performance. Consequently, the challenge lies in efficiently utilizing the I/O system to allow these applications to scale. When the volume of data increases, access can generate high training latency and add overhead significantly when data exceeds the main memory capacity. Therefore, it is essential to analyze the behavior of the I/O patterns generated during the training stage by reading the data set to analyze the behavior when the application scales and what amount of resources it will need. The paper presents a methodology to analyze parallel I/O patterns in Deep Learning applications in this context. Our methodological approach mainly aims at providing users with complete and accurate information. This involves a thorough understanding of how the application, the dataset, and the system parameters can significantly influence the parallel I/O of their deep learning application. We seek to empower users to make informed decisions through a structured methodology that allows them to identify and modify configurable elements effectively.

Hospital Emergency Departments (EDs) are one of the more complex units of the healthcare system, requiring coordination of human and technical resources for managing situations effectively. This article establishes the basic principles to design primitives of an Agent-Based Modeling and Simulation (ABMS) modular system, inspired by the modularity of Lego blocks, which allows the creation of computational models that can be employed as Decision Support Systems (DSS). The ABMS modular system has shifted from a monolithic approach to an adjustable system. This means that the system allows the description of the metasystem and agent box used to build the computational models (simulator) that, used as DSS, can help EDs managers to achieve the highest possible level of service quality, given the available resources.

A reference model is a model of something that contains a fundamental objective or idea of something and can be established as a reference for multiple purposes. We performed an analysis based on a reference model following the Spanish Ministry of Health’s document of standards and recommendations to achieve better care, efficiency, and uniformity in emergency services. This standard describes the guidelines and resources needed in hospital emergency services for better patient care. We also analyzed the standards of emergency services in the following countries: the United States, the United Kingdom, Germany, Canada, Paraguay, Argentina, the United Arab Emirates, and Turkey. The objective of the research is to analyze the efficiency of the Spanish model following its reference standard compared to the standards of other countries, to explore how the Spanish emergency service would work using the parameters of the emergency service standards compared to the standards of different countries, specifically the KPI we analyzed is the Door to the Doctor (DtD), through simulation. It was concluded that in some countries, DtD times improve compared to the Spanish reference standard, and in some cases, they worsen.

Deep Learning applications have become an important solution for analyzing and making predictions with massive amounts of data in recent years. However, this type of application introduces significant input/output (I/O) loads on computer systems. Moreover, when executed on distributed systems or parallel distributed memory systems, they handle much information that must be read during training. This persistent and continuous access to files can overwhelm file systems and negatively impact application performance. A file format defines how information is stored, and the choice of a format depends on the use case. Therefore, it is important to analyze how the file format influences the training stage when loading and reading the dataset, as opening and reading many small files could affect application performance. Thus, this paper will analyze the I/O pattern of different file formats used in deep learning applications to characterize their behavior.

A country’s population pyramid can affect the quality and demand for emergency departments in several ways. Emergency departament (ED) may need specialized resources and equipment to meet the population’s needs. In this research, we present an analysis of scenarios through agent-based modeling and simulation. We analyzed the population pyramids of Spain, Argentina, and Paraguay to provide insight into how demographic structure impacts the length of stay (LoS) and the need for medical and nursing staff. This can help policymakers and health managers better plan health resources and services in each country. We verified the evolution of the parameters, length of stay (LoS), and the occupation of doctors and nurses depending on different scenarios, such as the age of the patients and the number of patients arriving at the hospital, and how it can lead to saturation of the ED. Through several scenarios analyzed through simulations, we were able to conclude that the age pyramid of the patients treated at the ED of a hospital affects the demand for services, the complexity of cases, the need for specialized care in human and material resources, as well as waiting time and congestion in the ED. Saturation in the ED due to increased patient arrivals can negatively affect the quality of medical care (due to the shortage of human resources, materials, beds, and boxes), patient safety, and the saturation of medical and nursing staff. Implementing measures to manage demand and optimize available resources effectively is essential to ensure adequate care for all patients in EDs.

In high-performance computing (HPC) environments, efficient execution of AI applications is critical for optimal performance and resource utilization. In this work, we extend the PAS2P methodology to AI applications through message passing on HPC Cloud systems, defining the AI Application Model to describe their performance behavior. This extension identifies phases within AI applications, enabling analysis to focus on these phases instead of the entire application. By concentrating on them, we can better evaluate AI application efficiency, providing insights into system performance and guiding future optimizations for large-scale AI tasks on HPC infrastructure.

A country's population pyramid can affect the quality and demand for emergency departments (ED) in several ways. EDs may need specialized resources and equipment to meet the population's needs. In this research, we present an analysis of scenarios through agent-based modeling and simulation. We analyzed the population pyramids of Spain, Argentina, and structure impacts the length of stay (LoS) and the need tient care.

Accessing large volumes of data presents a significant challenge when finding the best strategies to manage the data efficiently. Deep learning applications require the processing of massive amounts of data, which implies a considerable access Input/Output (I/O) load on computer systems. During training, interaction with the I/O system intensifies as files are continuously accessed to read data sets. This persistent access could overload the file system, which, in turn, adversely impacts application performance and efficient storage system utilization. Several factors influence the I/O of these applications, and one of the most relevant is the variety of file formats in which datasets can be stored. The choice of file format depends on the use case, as each format defines how information is stored. Some file formats have features that promote efficient access to datasets during the training phase, which can improve the performance of deep learning applications. Likewise, it is also important that the format adapts to the context, in this case, to an HPC system with a parallel file system. We will propose an image preprocessing method for cases where performance improves with parallel file access. This method will transform image data sets from their original JPEG format to the more efficient HDF5 format. Thus, our research will focus on the importance of understanding the mode of data access, spatial and temporal patterns, and the level of parallelism in file access to determine whether it is advisable to change the storage format.

Distributed deep learning (DDL) applications generate heavy input/output (I/O) workloads that can create bottlenecks in high-performance computing (HPC) systems. Their optimal I/O configuration depends on factors such as access patterns, storage hardware, dataset size, and execution scale. This study proposes a systematic methodology for characterizing and optimizing I/O behavior in DDL applications, represented through the deep learning I/O benchmark (DLIO), and validated with the real DeepGalaxy application. We evaluate access modes, file formats, and Lustre file system configurations, demonstrating that stripe counts optimized for the access pattern and application scale can reduce I/O and execution times, achieving up to 18 GiB/s of bandwidth and a 5X increase in IOPS. HDF5 provides balanced performance, while TFRecord stands out in bandwidth-intensive scenarios. Shared access minimizes contention and improves scalability in multi-node executions. The results are consolidated into configuration guidelines that offer practical recommendations for practitioners to tune DDL applications for efficient execution in HPC environments.

This paper presents a structured methodology for the development of agent-based modeling and simulation (ABMS) systems, aimed at improving modularity and reusability in complex system representations such as Lego® pieces. The methodology addresses the challenge of translating real-world problems into executable ABMS models by defining standardized tables for key model components: environment, agents, interactions, and decision logic. Each element is described using modular structures that facilitate both conceptual validation (by domain experts) and computational validation (through simulation testing). A case study focused on a simplified hospital Emergency Department (ED) illustrates the practical application of this framework, showcasing how agent behaviors and system dynamics can be clearly represented and adapted. The approach emphasizes reusability and automation potential, bridging the gap between conceptual design and code-level implementation.

Emergency Departments (EDs) face increasing complexity due to rising patient demand, resource constraints, and the need for efficient service coordination. Traditional simulation models, while useful, cannot be easily adapted to a different hospital environments, making it difficult to transfer and scale solutions. Based on previous work, with a simulator working in a hospital, this work describes a modular Agent-Based Modeling and Simulation (ABMS) approach for increasing flexibility adaptation and reuse in ED simulations. The proposed technique, which deconstructs existing models into individual components, will allow hospitals with different workflows and operational constraints to construct customized simulations. To validate this methodology, we develop a structured, modular framework using NetLogo and Python. The suggested metasystem enables adaptive simulation-based decision assistance for emergency departments, which improves resource allocation and operational planning.

Performance modeling of parallel applications is essential for optimizing resource usage in high-performance computing (HPC) systems. However, some scientific applications exhibit irregular performance behaviors, which complicates cre-ating accurate characteristic models. This irregularity is mainly due to these applications' nondeterministic computational and communication patterns. Tools such as PAS2P (Parallel Application Signatures for Performance Prediction) are used to extract detailed information about parallel applications. PAS2P is based on the repetitive behavior of the application to analyze and predict the application's performance, using the same resources that the parallel application uses for its execution. This paper presents a characterization model based on the PAS2P methodology for irregular applications that groups the repeatability patterns of all the processes running the application into a single characteristic model. To achieve this, we consolidate the different characterizations performed by each process independently, using metrics such as the number of instructions, the execution time of relevant sections, and the topological characteristics of the application. By grouping these repeatability patterns of all processes, we can obtain a concise and accurate representation of the behavior of irregular applications, thus improving predictability and performance optimization in HPC systems.

Generally, evaluating the performance offered by an HPC I/O system with different configurations and the same application allows selecting the best settings. This paper proposes to use agent-based modeling and simulation (ABMS) to evaluate the performance of the I/O software stack to allow researchers to choose the best possible configuration without testing the real system. Testing configurations in a simulated environment minimizes the risk of disrupting real systems. In particular, this paper analyzes the communication layer of an HPC I/O system, more specifically the communication layer of the parallel file system (PVFS2). ABMS has been selected because it enables a rapid change in the level of analysis for modeling and implementing a simulator. It can focus on both macro- and micro-levels (how the aggregate behavior of system agents is born and analyzing their individual behavior).

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA’s parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

The exponential growth of data handled by Deep Learning (DL) applications has led to an unprecedented demand for computational resources, necessitating their execution on High Performance Computing (HPC) systems. However, understanding and optimizing Input/Output (I/O) of the DL applications can be challenging due to the complexity and scale of DL workloads and the heterogeneous nature of I/O operations. This paper addresses this issue by proposing an I/O traces processing method that simplifies the generation of reports on global I/O patterns and performance to aid in I/O performance analysis. Our approach focuses on understanding the temporal and spatial distributions of I/O operations and related with the behavior at I/O system level. The proposed method enables us to synthesize and extract key information from the reports generated by tools such as Darshan tool and the seff command. These reports offer a detailed view of I/O performance, providing a set of metrics that deepen our understanding of the I/O behavior of DL applications.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition characterized by inflammation and narrowing of the airways, leading to symptoms such as shortness of breath, coughing, and chest tightness. Treatment typically involves lifestyle adjustments, medication, and pulmonary rehabilitation to improve lung function and quality of life. This study presents a model examining COPD patient behavior within cohort-based strategies, focusing on how environmental factors impact vital signs across the entire cohort. We developed a comprehensive virtual clinical trial model that encompasses study protocol design, participant recruitment, virtual data collection, outcome analysis, and conclusions. This includes remote symptom monitoring, virtual healthcare consultations, treatment adherence assessments, and research data collection. Additionally, we explore the influence of external variables such as environmental conditions, comorbidities, and lifestyle factors on chronic disease symptoms and disease stability. We used an Agent-Based Model(ABM) to incorporate these factors to assess COPD progression and treatment efficacy. Individual agents represent COPD patients, each characterized by attributes such as age, smoking history, lung function, comorbidities, and treatment plans.

The fault tolerance method currently used in High Performance Computing (HPC) is the rollback-recovery method by using checkpoints. This, like any other fault tolerance method, adds an additional energy consumption to that of the execution of the application. The objective of this work is to determine the factors that affect the energy consumption of the computing nodes on homogeneous cluster, when performing checkpoint and restart operations, on SPMD (Single Program Multiple Data) applications. We have focused on the energetic study of compute nodes, contemplating different configurations of hardware and software parameters. We studied the effect of performance states (states P) and power states (states C) of processors, application problem size, checkpoint software (DMTCP) and distributed file system (NFS) configuration. The results analysis allowed to identify opportunities to reduce the energy consumption of checkpoint and restart operations.