distributed systems

This paper substantiates an approach to performing calibration procedures for measurement channels on-site in distributed cyber-physical systems. A conceptual model of a local metrological supervision system is proposed, which provides calibration considering real operating conditions. Special attention is paid to the use of code-controlled measures to generate reference signals and a centralized module for remote control, data processing, and storage.

The rapid expansion of the Internet of Things (IoT) has resulted in a substantial increase of diverse data from distributed devices. This extensive data stream makes it increasingly important to implement robust and efficient real-time anomaly detection techniques that can promptly alert about issues before they could escalate into critical system failures.

The increasing complexity of cyber threats requires the development of effective methods for detecting and classifying attacks in network traffic. This study analyzes the effectiveness of three popular machine learning algorithms: Random Forest, which is used for anomaly detection, Support Vector Machines (SVM), which performs cyber threat classification, and autoencoders, which are used for data preprocessing and deep traffic analysis.

The article discusses key information security principles, focusing on confidentiality, integrity, availability, traceability, and the DIE model (Distributed, Immutable, Ephemeral). Confidentiality emphasizes the importance of secrecy and controlling access to prevent sensitive information from misappropriation. Integrity ensures that data remains accurate and trustworthy, with measures to prevent unauthorized modifications.

The article addresses the issue of data protection in information and communication systems amid the growing volume of traffic and the increasing number of cyber threats, necessitating improvements in the effectiveness of intrusion detection and prevention systems. Various types of Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), their advantages, and disadvantages are considered. The methods of threat detection are analyzed, including signature-based methods, anomaly detection methods, and machine learning-based methods.

Quality of Service (QoS) is identified as a key task for distributed systems because meeting user needs is an important aspect of their successful functioning. Most container autoscaling solutions focus on resource optimization and cost management. However, these solutions often do not consider the dynamic user requirements for Quality of Service (QoS), resulting in delays in resource allocation and a decrease in service quality. Existing autoscaling and load balancing algorithms inadequately account for load dynamics, which is a significant issue.

The emergence of microservice architecture has revolutionized software development practices by decentralizing components, facilitating scalability, and enabling agility in system design and deployment. There are some benefits of incorporating microservices instead of a single server, however, distributed components introduce extra constraints and complexities in maintaining data consistency as well. As microservices interact independently, coordinating data updates across multiple services becomes challenging, particularly in scenarios where transactional integrity is required.

The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems.