Перегляд за Автор "Rolik, Oleksandr"

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    Decentralized Task Allocation Method in Hierarchical IoT Systems Using Fuzzy Logic
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Rolik, Oleksandr; Nahaiko, Dmytro
    The use of fog and edge computing extends the computational capabilities of IoT systems to the network edge, contributing to the minimization of delays during task execution. Osmotic computing complements distributed computing by providing seamless integration between computational environments through dynamic migration of micro-elements across different hierarchy tiers according to current load conditions and resource availability. However, within the concept of osmotic computing, a key challenge remains the effective management of task allocation under conditions of uncertainty, dynamism, and heterogeneity of the IoT environment. The aim of this study is to improve the efficiency of resource utilization and task allocation in hierarchical IoT systems based on osmotic computing under uncertain and dynamically changing environmental conditions. The object of the study is the process of task allocation in multi-tier IoT systems that include cloud, fog, and edge computing. The subject of the study is methods and models for task allocation and computing resource management in IoT systems using the osmotic computing paradigm. The paper presents a three-tier hierarchical management model built on cloud, fog, and edge environments, which implements a centralized-decentralized management approach. Each tier is represented by a set of computing nodes and a management system that performs local task allocation, resource state monitoring, and micro-element management. The management system of the lower tier is subordinate to the higher-tier management system in the hierarchy. A method for decentralized task allocation in hierarchical IoT systems using fuzzy logic has been developed. The allocation method includes two decision-making stages using a fuzzy inference system: determining the direction of task allocation and selecting the optimal computing node for its execution. The determination of task allocation direction is carried out based on task characteristics, and the suitability rating of computing nodes is determined considering task execution latency, resource utilization efficiency, and load balancing. The task is assigned to the node with the maximum rating. The use of fuzzy logic ensures rational decision-making under conditions of uncertainty in real-time, which is characteristic of highly heterogeneous and dynamic IoT environments. Experimental modeling and investigation of the method were carried out using the iFogSim simulation environment. The research results show that the percentage of locally executed tasks remains virtually unchanged with different numbers of tasks, indicating stability in decision-making. Increasing the intensity of task generation leads to an increase in task computation latency due to increased load on computing nodes, while task assignment latency and response latency remain unchanged. The method demonstrated adaptability in task allocation for different types of tasks.
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    Hexacopter-Based Cyber-Physical System for Water Sampling with Adaptive Path Planning and Multi-Drone Coordination
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Pysarenko, Andrii; Rolik, Oleksandr
    The object of this study is a hexacopter-based cyber-physical system designed for autonomous water sampling to support environmental monitoring, addressing the problem of inefficient control under dynamic conditions. The subject focuses on integrating physical flight control and water sampling operations with cyber supervisory functions, including real-time waypoint navigation, task scheduling, and multi-drone coordination, validated as a current system component. The research investigates the system’s performance under payload variations and wind disturbances, ensuring robustness and precision in adverse environments. The purpose is to improve efficiency of water sampling through this CPS, achieving enhanced flight stability and positioning accuracy via a cascade PID control system, optimizing mission planning with adaptive cyber strategies, and increasing scalability through multi-drone operations. This approach aims to surpass traditional UAV systems by using physical-cyber integration for precise, robust, and scalable water quality assessment. The methodology combines simulation-based and analytical techniques to develop and assess the hexacopter CPS. A 6-degree-of-freedom mathematical model, based on Newton-Euler equations, was constructed in MATLAB/Simulink to simulate hexacopter dynamics, incorporating payload and wind effects. The cascade PID control system was tuned using the Ziegler-Nichols method, with iterative optimization to reduce overshoot and settling time across three scenarios: 1 kg static payload, 1.5 kg dynamic payload, and 5 m/s wind. The cyber supervisory system, implemented in ROS 2, employs graph-based algorithms (Dijkstra’s for waypoint navigation, list-scheduling for task allocation) and a consensus protocol for multi-drone coordination, tested in a 500x500 m² environment. Performance metrics, such as position root mean square error (RMSE) and attitude errors, were analyzed to evaluate system effectiveness. Results demonstrate significant improvements in water sampling capabilities. The cascade control system achieved a 40–50% reduction in position RMSE and maintained attitude errors within ±0.8° to ±1.2°, ensuring stable flight. The cyber-physical framework reduced mission time by 15% through adaptive path optimization, while multi-drone coordination increased sampling coverage by 20%, enhancing scalability. These outcomes reflect the system’s precision and robustness that highlight novel control and coordination strategies with practical value for environmental monitoring. The study provides a foundation for future ecological applications.
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    Method of horizontal pod scaling in Kubernetes to omit overregulation
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2024) Rolik, Oleksandr; Volkov, Volodymyr
    This paper describes the method of omitting the over-regulation effect that occurs under certain conditions by horizontal pod autoscaling microservices in the container application orchestration system Kubernetes. The effect was initially observed only for long-term HTTP WebSocket sessions, where it led to excessive use of computing resources, which reduced the efficiency of IT infrastructure management, and caused service failure. It was found that the overregulation effect is reproduced not only for connections with long-term HTTP sessions, such as HTTP WebSocket, but also for shorterterm REST HTTP sessions in case of increased delay in the metric collection cycle used for horizontal pod autoscaling. It is assumed that this effect happens due to the approach of implementing horizontal scaling controllers similar to the principles of proportional regulators in systems with negative feedback from the theory of automation and control. It is proposed to extend one of the methods used for optimizing the proportional controller to the problem consisting of reducing the time delay between scaling metrics collecting and upscale applied by the controller in Kubernetes. The applied method demonstrated its effectiveness, therefore, within the same methodology, an experiment was conducted on using the proportional-integral-differential controller for automatic horizontal scaling of pods. The results obtained showed why the proportional-integral-differential controller is not widespread among the overviewed Kubernetes solutions for horizontal automatic scaling. An assumption was made about the limitations of studying the downscaling process in Kubernetes due to the need to consider the quality of service when stopping pods and the need to collect indicator metrics using quality-of-service object management tools such as ISTIO.