This study addresses the critical challenge of proactive anomaly detection and efficient resource management in infrastructure observability. Introducing an innovative approach to infrastructure monitoring, this work integrates machine learning models into observability platforms to enhance real-time monitoring precision. Employing a microservices architecture, the proposed system facilitates swift and proactive anomaly detection, addressing the limitations of traditional monitoring methods that often fail to predict potential issues before they escalate. The core of this system lies in its predictive models that utilize Random Forest, Gradient Boosting, and Support Vector Machine algorithms to forecast crucial metric behaviors, such as CPU usage and memory allocation. The empirical results underscore the system's efficacy, with the GradientBoostingRegressor model achieving an R² score of 0.86 for predicting request rates, and the RandomForestRegressor model significantly reducing the Mean Squared Error by 2.06% for memory usage predictions compared to traditional monitoring methods. These findings not only demonstrate the potential of machine learning in enhancing observability but also pave the way for more resilient and adaptive infrastructure management.

In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

The emergence of quantum computing challenges traditional security mechanisms, particularly in heterogeneous and resource-constrained IoT and smart environments that must satisfy DN25 requirements. This work introduces a reinforcement learning-driven model for the adaptive selection and orchestration of cryptographic algorithms. Acting as an intelligent decision layer, the system observes contextual, network, and operational metrics to recommend or enforce configurations combining classical schemes, post-quantum cryptography, and Quantum Key Distribution when available. The selection problem is formulated as a Markov Decision Process with state and action spaces aligned with protocol control flows and is embedded into a security middleware with negotiation and fallback mechanisms to ensure interoperability and policy compliance without modifying application logic. Experimental results demonstrate that the model dynamically adjusts key lengths, algorithm families, and security policies according to risk and resource conditions, increasing post-quantum cryptography and Quantum Key Distribution usage by up to 33.4% and 23.9% in high-risk scenarios while favoring low-latency classical or hybrid options for less critical traffic. The system achieves success rates above 78% while maintaining stable latency and resource usage during extended operation.

Memory optimization in Java applications is essential for performance and scalability. This paper investigates the efficiency of the StringDeduplication parameter in JVM versions 11, 17, and 21, using a Web Crawler developed in Spring Boot. The results show that the efficiency of StringDeduplication decreased from 34.3% deduplication in version 11 to 3.4% in version 21, with an increase in deduplication time from 1,264 ms to 3,439 ms. To mitigate this problem, a custom solution in C was developed for JVM version 21, which increased deduplication to 31.1% and saved 110.2 MB of memory. The main scientific contribution of this work is the identification of the loss of efficiency of StringDeduplication in the latest JVM versions and the proposal of a custom solution that improves string deduplication, offering a viable alternative for developers and software engineers.