Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER’s ...Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.
•Probabilistic method for choosing an optimal cloud deployment option.•Equivalence classification of available cloud deployment options.•Model-checking approach to verify decision-making ...results.•Experimental study comparing the new probabilistic method with a baseline method.
Context: Existing software workbenches allow for the deployment of cloud applications across a variety of Infrastructure-as-a-Service (IaaS) providers. The expected workload, Quality of Service (QoS) and Non-Functional Requirements (NFRs) must be considered before an appropriate infrastructure is selected. However, this decision-making process is complex and time-consuming. Moreover, the software engineer needs assurances that the selected infrastructure will lead to an adequate QoS of the application.
Objective: The goal is to develop a new method for selection of an optimal cloud deployment option, that is, an infrastructure and configuration for deployment and to verify that all hard and as many soft QoS requirements as possible will be met at runtime.
Method: A new Formal QoS Assurances Method (FoQoSAM), which relies on stochastic Markov models is introduced to facilitate an automated decision-making process. For a given workload, it uses QoS monitoring data and a user-related metric in order to automatically generate a probabilistic model. The probabilistic model takes the form of a finite automaton. It is further used to produce a rank list of cloud deployment options. As a result, any of the cloud deployment options can be verified by applying a probabilistic model checking approach.
Results: Testing was performed by ranking deployment options for two cloud applications, File Upload and Video-conferencing. The FoQoSAM method was compared to a baseline Analytic Hierarchy Process (AHP). The results show that the first ranked cloud deployment options satisfy all hard and at least one of the soft requirements for both methods, however, the FoQoSAM method always satisfies at least an additional QoS requirement compared to the baseline AHP method.
Conclusions: The proposed new FoQoSAM method is appropriate and can be used in decision-making when ranking and verifying cloud deployment options. Due to its practical utility it was integrated into the SWITCH workbench.
Modern component-based software engineering environments allow deployment of cloud applications on various computing infrastructures, such as Edge-to-Cloud infrastructures. The heterogeneous nature ...of such computing resources results in variable Quality of Service (QoS). Therefore, the deployment decision can seriously affect the application’s overall performance. This study presents an approach for automated deployment of cloud applications in the Edge-to-Cloud computing continuum that considers non-functional requirements (NFRs). In addition, the authors explore multiple methods for selection of optimal cloud infrastructure, such as IaaS. The paper presents an experimental evaluation performed using a cloud application for storing data under different workloads. For the purposes of the experimental evaluation, a Kubernetes cluster composed of 44 computing nodes was used. The cluster nodes were geographically distributed computing infrastructures hosted by several service providers. The proposed approach allows a reliable selection of infrastructures, which satisfy high QoS requirements for cloud applications, from heterogeneous Edge-to-Cloud computing environments. Современные среды разработки программного обеспечения на основе компонентно-ориентированного программирования позволяют беспрепятственно развертывать облачные приложения в различных вычислительных инфраструктурах, таких как Edge-to-Cloud. Неоднородная природа таких вычислительных ресурсов приводит к непостоянному качеству обслуживания (QoS). Поэтому решение о развертывании приложения может серьезно повлиять на его общую производительность. В статье рассмотрен подход к автоматизированному развертыванию облачных приложений в вычислительном континууме Edge-to-Cloud, учитывающий нефункциональные требования (NFR). Исследованы способы выбора оптимальной услуги с точки зрения ожидаемого качества обслуживания. Экспериментальная оценка проведена с помощью облачного приложения для хранения данных в трех случаях с разной нагрузкой. Проведены эксперименты на кластере Kubernetes, состоящем из 44 вычислительных узлов (облачных инфраструктур). Узлы кластера были географически распределены в нескольких местах и размещались несколькими поставщиками услуг. Подход позволит надежно выбирать инфраструктуры из гетерогенных Edge-to-Cloud сред, удовлетворяющих требованиям к качеству обслуживания облачных приложений.
Modern possibilities of using AI methods in the analysis of biomedical data Gasanova, Ilakha A; Prelovskii, Dmitrii S; Yurkin, Vladimir A ...
St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems,
01/2020, Letnik:
13, Številka:
4
Journal Article
Recenzirano
Nowadays, one of the key indicators that have a great impact on the evolution of society is artificial intelligence. AI and Big Data technologies are widely used to analyze biomedical data. This ...article describes what artificial intelligence and Big Data are and what are the modern possibilities of using their methods and technologies. The statistics showing the growth in the use of Big Data and AI technologies in medical research are presented. The main types of artificial neural networks used in this area are considered, as well as examples of the successful use of Big Data technologies in medicine. The effectiveness of the use of special computer programs in the field of health care, which allows detecting diseases at early stages, are demonstrated. The key technological and ethical problems of introducing artificial intelligence technologies into medicine are considered, the difficulties of implementation, integration and dissemination of technologies are shown. Special attention is paid to the use of AI in the fight against the global pandemic, the Covid-19 coronavirus infection. The methods of using AI in various countries for collecting data, analyzing and then building a model of the spread and mutation of coronavirus under various scenarios of the development of the situation and the introduction of special restrictive measures, as well as predicting their effectiveness are analyzed.
Described in the paper is an approach to symbolic test scenarios concretization in the scope of automated software verification and testing technology. Tools for automated concretization process ...based on user defied settings are presented.
We have considered an approach to estimating the performance for a wide range of science applications calculated on modern HPC systems with globally addressed memory. Modeling and estimation of ...memory bandwidth have been examined for a set of applications with parallel structure based on MPI/OpenMP technology. The HPCG benchmark was used to create a workload representing a wide range of calculation and communication tasks in science applications. A set of experiments for checking the model on a real HPC system with globally addressed memory (ccNUMA architecture with 12 Tb of memory with single image of operating system installed) was conducted for estimating the size of the task and highlighting the benefits of optimized model usage. The optimized model will allow to estimate the performance of modern and future systems developed based on the ccNUMA architecture which contains 24 Tb of memory in one node. The model will also allow to compare the results of NUMA systems with other modern HPC architectures. Citation: Drobintsev P.D., Kotlyarov V.P., Levchenko A.V. Experimental aspects of memory bandwidth for HPC systems with ccNUMA architecture. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems. 2017, Vol. 10, No. 3, Pp. 32-41. DOI: 10.18721/JCSTCS.10303
The CdSe and ZnSe:Mn colloidal quantum dots (QDs) have been synthesized in order to use them as a contrast agent for bioimaging. The synthesis of QDs was made in the aqueous solution. These compounds ...are fluorescent semiconductor nanoparticles and are held to be promising fluorophores which can be used as an important research tool in biology and medicine. They can be exploited to allocate the problematic biological tissues and individual cells. Their applicability to human examination was studied. For this purpose we investigated the morphological changes in the cells by reacting with the CdSe/l-Cys and ZnSe:Mn/MPA quantum dots. The cytotoxicity of CdSe/l-Cys in the line of breast carcinoma was examined using confocal microscopy. The results can be seen as encouraging.
Predicting RTS Index Futures Using Machine Learning Voinov, Nikita V.; Voroshilov, Maksim K.; Molodyakov, Sergey A. ...
2021 XXIV International Conference on Soft Computing and Measurements (SCM),
2021-May-26
Conference Proceeding
The results of the approach to predicting RTS index futures based on linear and polynomial regression are presented. Two modifications of the proposed algorithm are described. Based on the analysis ...of the obtained results some recommendations and conclusions are made on using of the developed approach. Also presented in the paper is an overview of the most popular stock market prediction methods based on machine learning as well as the results of their usage.
Usage of industrial networks of Internet with net-centric control is the driving trend of the future material manufacturing of goods and services. The bright future of this approach is out of doubt ...provided these complex net-centric systems will use adaptive approach to planning of manufacturing scenarios and function with high reliability. The issue here is that such systems are characterized by complex multi-parameter operability modes controlled by a large number of criteria. The paper considers an approach to providing reliable management of complicated IoT systems. This is achieved by solving multi-criteria tasks over many processes of various physical nature. Corresponding methods of hierarchical decomposition of multi-criteria tasks, levels of the process of multi-criteria optimization, specifics of aggregation levels and the master equation (algorithm) of the optimization process are described.
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