AWS

This article presents a new scalable system for automated modeling of physical processes and experiment management based on microservice architecture and cloud technologies. The proposed platform addresses the growing need for flexible, cost-effective, and highly scalable computational solutions for physical research. The system utilizes Amazon Web Services cloud infrastructure with containerized microservices to provide automated resource allocation, experiment orchestration, and laboratory equipment integration.

The rapid expansion of cyber-physical systems (CPS) has intensified the need for scalable and adaptive mechanisms to collect heterogeneous environmental data from numerous unstable external sources. Traditional serverless orchestration frameworks, while elastic and cost- efficient, lack runtime adaptability and feedback-awareness, leading to inefficiencies under dynamic API conditions. This paper presents a novel adaptive orchestration model for serverless data collection pipelines, driven by metadata configuration and continuous feedback control.

Due to the need to store large amounts of data, automate processes, support business continuity, and organize remote work, there is a need for a deep understanding of the concepts and capabilities of cloud computing. The use of this technology is becoming a key factor in the competitiveness of enterprises in the digital age.

The paper examines the explores the use of artificial intelligence (AI) methods and tools for the efficient construction, management, and optimization of cloud IT infrastructures. The main challenges related to the automation of deployment, scaling, monitoring, and resource optimization in the cloud environment are analyzed, along with the role of AI in addressing these issues. Approaches to integrating AI to improve productivity, reduce operational costs, and enhance the security of cloud platforms are discussed.

Due to the constant development of information technology and the increasing volume of digital data, the concept of serverless systems has become relevant and promising in the field of software development. Serverless systems, also known as Serverless, are a new approach to deploying and managing applications. Developers can focus on developing functions without spending extra time managing servers and infrastructure. This approach is appropriate for various applications, including data processing, and is particularly useful for collecting and processing specialized data.

The Internet of Things (IoT) enables the creation of networks between devices, people, applications, and the Internet, creating new ecosystems with higher productivity, better energy efficiency, and higher profitability. Nodes in these networks must have the ability to communicate and exchange data, which requires the use of data transfer protocols. However, choosing the right protocol for a specific use case is not always straightforward.

An existing goods accounting information system was assessed for possible infrastructure optimization. A various parts of the system were analyzed to improve infrastructure costs without having a significant degradation of non-functional requirements. Modeling of the optimized system was performed, and evaluation of the infrastructure costs was made. Several optimization directions were evaluated, analyzed and either recommended or rejected.

This paper is devoted develop software for time series forecasting using Python programming language. SARIMA model was used to develop the system.

Serverless computing is a new and still evolving type of cloud computing, which brings a new approach to the development of information systems. The main idea of serverless is to give an approach of doing computing without dealing with a server to a user. Such approach allows to reduce the cost of the system building and system support. It allows small companies to concentrate on their own system designing instead of thinking about infrastructure building and supporting. Also, a big problem of providing the system security on high level is on cloud’s provider engineering support service.