The large popularity of services offered by cloud computing (CC) requires constant expansion of its physical infrastructure. At the same time, CC operators apply various mechanisms that enable the existing physical resources to be optimally used. Useful tools in the design and optimization process are analytical and simulation models. They allow information about the operation of CC to be obtained without the need to make changes to the physical infrastructure. This article presents and discusses a multiservice, multiparameter model of a CC physical infrastructure that provides services of the infrastructure-as-a-service (IaaS) type. In the proposed model, the following four elements are used to specify network settings and describe the demands necessary to activate a virtual machine: the number of processors, the capacity of RAM, the HDD capacity, and the required network bitrate. Such an approach gives the opportunity to describe accurately the use of resources in real servers. To verify and validate the proposed model, we developed and implemented a simulator of the physical infrastructure of CC. The results of the simulations confirm the validity of all the theoretical assumptions adopted in the model. The proposed model can also serve as a tool, in the form of an appropriate application, for determining the resources that are necessary to service calls at the required loss level.

This paper presents an analytical model of a multiservice queueing system that services elastic and adaptive BPP traffic. The model presented in the paper was developed as an extension of earlier works published by the authors. The model is based on several concepts specific to modelling multi-service systems. In order to derive the model, the idea of equivalent bandwidth and the discretisation process were used first. Subsequently, the MIM-BPP method was generalised in relation to a system with traffic that undergoes the compression mechanism. Since the proposed model is an approximate one, the results of the calculation are compared with the simulation results. The comparison of the results confirms the accuracy of the proposed model.

Cloud computing is among the fastest-developing areas of Information and Communication Technologies (ICT). The increasing number of users and the number of services offered by the network makes it necessary to develop new methods of network modeling and analysis. The paper presents a concept of model of cloud computing based on the Erlang's Ideal Grading (EIG) model.

Disease outbreaks and pandemics show us how important it is to limit the spread of diseases. One common indicator of many ailments is body temperature. It’s a measurement that can be taken quickly, also using contactless methods. However, it is necessary to ensure the methodological correctness, repeatability and reliability of such measurement. In this manuscript, we introduce a non-intrusive approach for individual body temperature assessment that adheres to the stipulated criteria outlined by ISO/IEC 80601-2-59 standard. The measurements are performed at specific regions of interest (ROIs) of a human face, at the inner canthi of both eyes, which show high robustness to the environment temperature change. The method utilises the fusion of RGB-D (red, green, blue and depth) and thermal cameras. The system detects the ROIs on the RGB image employing deep learning methods and transfers them to the thermal image, from which the temperature can be read. The system was tested on our validation dataset consisting of 210 individuals, achieving ROI’s position identification mean error below 3 mm and temperature measurement error below 0.5°C, which is in line with the ISO norm requirements.

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users’ virtual machines (VMs) are run on the service provider’s physical infrastructure, making it possible to become independent of the need to purchase one’s own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems.