Under many scenarios where resources may be scarce or a good Quality of Service is a requirement, appropriately sizing components and devices is one of the main challenges. New scenarios, such as IoT, mobile cloud computing, mobile edge computing or fog computing, have emerged recently. The ability to design, model and simulate those infrastructures is critical to dimension them correctly. Queuing theory models provide a good approach to understanding how a given architecture would behave for a given set of parameters, thus helping to detect possible bottlenecks and performance issues in advance. This work presents a fog-computing modelling framework based on queuing theory. The proposed framework was used to simulate a given scenario allowing the possibility of adjusting the system by means of user-defined parameters. The results show that the proposed model is a good tool for designing optimal fog architectures regarding QoS requirements. It can also be used to fine-tune the designs to detect possible bottlenecks or improve the performance parameters of the overall environment.

Cloud systems and microservices are becoming powerful tools for businesses. The evidence of the advantages of offering infrastructure, hardware or software as a service (IaaS, PaaS, SaaS) is overwhelming. Microservices and decoupled applications are increasingly popular. These architectures, based on containers, have facilitated the efficient development of complex SaaS applications. A big challenge is to manage and design microservices with a massive range of different facilities, from processing and data storage to computing predictive and prescriptive analytics. Computing providers are mainly based on data centers formed of massive and heterogeneous virtualized systems, which are continuously growing and diversifying over time. Moreover, these systems require integrating into current systems while meeting the Quality of Service (QoS) constraints. The primary purpose of this work is to present an on-premise architecture based on Kubernetes and Docker containers aimed at improving QoS regarding resource usage and service level objectives (SLOs). The main contribution of this proposal is its dynamic autoscaling capabilities to adjust system resources to the current workload while improving QoS.

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.

The Model Context Protocol (MCP) enables Large Language Models (LLMs) to act as autonomous agents that orchestrate complex workflows over distributed systems, while MCP resolves integration bottlenecks by standardizing agent-to-resource communication. Its current registry relies on an unverified pointer architecture, exposing agentic workflows to supply chain poisoning and dynamic capability mutation (“Rug Pull”) attacks. This paper identifies this gap and proposes a three-layer architectural framework for a Trustworthy MCP Registry. The novelty of our contribution lies not in the individual standards employed (RFC 8615, Sigstore, and JCS/JWS are established technologies), but in their specific composition to address MCP’s unique runtime security requirements: (1) RFC 8615 Well-Known URIs for decentralized server discovery and domain-bound identity; (2) Sigstore Keyless signing to bind server artifacts to audited CI/CD environments without managing long-lived keys; and (3) JSON Canonicalization Scheme (RFC 8785) combined with JWS to provide deterministic, per-message integrity verification of live capability updates. We present a prototype implementation and an experimental evaluation conducted in a controlled, synthetic environment. Results indicate that the cryptographic overhead averages 0.61 ms per signing operation and that the Layer 3 mechanism correctly rejects all 100 simulated Rug Pull attempts, as expected by construction, since an attacker without the server’s private key cannot produce a valid signature. These findings suggest that the proposed approach is feasible and warrants further evaluation in real-world deployment settings.