distributed computing

The increasing adoption of multi-cloud architectures for machine learning (ML) workloads presents significant challenges in performance optimization and latency management across heterogeneous cloud environments. Contemporary organizations are increasingly leveraging multiple cloud service providers to optimize costs, enhance reliability, ensure vendor independence, and access specialized services. However, this strategic approach introduces complex performance management challenges that traditional single-cloud optimization techniques cannot adequately address.

This paper proposes a novel architecture of a multi-agent system and its formal specification for analyzing and adaptively correcting students' learning trajectories using software agents in digital learning environments. The proposed approach integrates artificial intelligence tools, tem- poral logic, and a multi-agent system architecture to ensure personalized adaptation of educational content.

The paper presents the implementation of an Apache Spark distributed computing cluster based on Raspberry Pi microcomputers. The solution consists of three Raspberry Pi 4 devices (one master node and two worker nodes), each equipped with 8 GB of RAM and a high-speed network connection. The cluster configuration was optimized by adjusting the SPARK_WORKER_MEMORY and SPARK_WORKER_CORES parameters to maximize the use of available hardware resources. Secure communication between nodes was established through authentication using 4096-bit SSH keys.

The paper presents approaches for creating an automated internet-accessible semiconductor laboratory, specifically the control software system that ensures the internet accessibility of the laboratory. The functionality and structure of the software-hardware complex, developed with consideration of known solutions, are described, as well as its implementation options using the internet, cloud, and edge computing. Local implementation options with enhanced resilience to force majeure factors and cyber threats are also considered.

Розглядається проблема пошуку оптимальних комбінаторних структур на прикладі дерев Ліча. Узагальнено критерії оптимальності для таких дерев. Запропоновано застосувати для пошуку систему розподілених обчислень на основі методу повного перебору. Наведено можливі варіанти алгоритмів пошуку, вказано їхні переваги та недоліки.

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis.

The problems of functioning of quality assurance systems in the 5th generation mobile networks are discussed. The method of quality control of transmission of video streams in the 5th generation networks based Smart Grid, which takes into account the quality requirements for video transmission in the telecommunications environment and uses advances in process quality control transmission of digital television is presented.