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All Journal Emerging Science Journal
Islam Alexandrov
NRC "Kurchatov Institute" - SRISA, Moscow 117218
Environmental Science

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Trusted AI-Based Method for Predicting Controller Load and PSO-Based Structure for Reducing Latencies Vladimir Zh. Kuklin; Islam Alexandrov; Maxim Mikhailov; Naur Z. Ivanov; Elena Yu. Linskaya
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-017

Abstract

The objective of this study is to develop a trusted AI-based framework for predicting software-defined networking (SDN) controller load and optimizing fog/edge microservice orchestration to reduce end-to-end latency in dense 5G scenarios. The proposed approach integrates user-aware spatial clustering with evolutionary resource selection to maintain stable quality of service (QoS) under high mobility and traffic variability. In the analysis stage, k-means clustering partitions users into spatial sectors and identifies sector centroids. Particle swarm optimization (PSO) is then applied to fog-node selection, resource sizing, and adaptive microservice placement and migration. To enhance system resilience, a recurrent neural network (RNN) is employed to forecast SDN controller load using correlation-informed features extracted from service-channel dynamics. Numerical experiments on heterogeneous fog-node topologies indicate that the framework reduces microservice execution time by 69% relative to baseline placement strategies under identical load conditions, while controller-load prediction attains an RMSE of 0.00387. These findings confirm the effectiveness of both the latency-reduction mechanism and the controller-load estimation workflow. The novelty of this work lies in the unified optimization of microservice placement, migration, and SDN controller-load anticipation within a single reproducible architecture, extending existing fog and edge orchestration approaches that typically address these components as independent subproblems.
Monte Carlo-Based Assessment of Machine Flexibility in Group-Configured Part-Feeding Systems Islam Alexandrov; Ilya Melikov; Nikita Karpov; Naur Ivanov; Dmitry Krasovsky; Alexander Shurpo
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-021

Abstract

Modern high-mix, low-volume manufacturing faces significant downtime during setup changes in part-feeding systems, yet no quantitative model currently exists that links group-based reconfiguration strategies to a measurable flexibility index under stochastic batch-size conditions. This study therefore aims to develop and experimentally validate a probabilistic mathematical model for assessing machine flexibility when a group-based reconfiguration approach is applied to part-feeding systems. The methodology combines Monte Carlo simulation to model random batch-size distributions with physical validation using a rotary orienting device across eight distinct sleeve types. Simulation results indicate that the proposed strategy reduces setup labor by 51-61% in systems handling 100 different part types. When fewer than one-third of parts require reconfiguration, the machine flexibility index reaches 0.088 ± 0.014, meeting established thresholds for high system flexibility. Experimental tests confirm that a uniform group-level adjustment maintains operational efficiency deviations within 3-5% across varying part geometries. The primary novelty of this work lies in introducing a confidence-bounded flexibility coefficient that explicitly incorporates auxiliary loading subsystems, which are consistently overlooked in existing deterministic approaches. This provides a practical, data-driven tool for production planning that enhances responsiveness without sacrificing throughput or increasing system complexity.
Co-Authors Alexander Shurpo Dmitry Krasovsky Elena Yu. Linskaya Ilya Melikov Maxim Mikhailov Naur Ivanov Naur Z. Ivanov Nikita Karpov Vladimir Zh. Kuklin