METHOD OF MULTI-CRITERIA OPTIMISATION OF DATA FLOW DISTRIBUTION IN SELF-ORGANISED TELECOMMUNICATIONS NETWORKS
DOI:
https://doi.org/10.26906/SUNZ.2026.1.206Keywords:
self-organising telecommunications networks, multi-criteria optimisation, data flow distribution, load balancing, multidimensional knapsack problem, evolutionary algorithms, resource optimisationAbstract
Relevance. Self-organising networks operate under variable topology and resource availability. It is necessary to jointly reduce delay and load imbalance while increasing resilience to failures and topology changes. This requires adaptive optimisation with near real-time decision-making. Object of the study: distribution processes of complex-structured data flows in self-organising telecommunications networks under node resource constraints. Purpose of the article: to develop a multi-criteria optimisation method for flow distribution that jointly reduces delay and load imbalance and increases resilience to failures and topology changes, taking into account heterogeneous QoS requirements and limited node resources. Research results. Each flow is represented as a set of subflows with heterogeneous QoS requirements. The limitations of computational and bandwidth resources of nodes are formalised as a multidimensional knapsack problem. The optimisation loop combines global evolutionary search with local refinement of solutions. Adaptive route restructuring is applied in response to traffic variability and network state changes. Simulation results confirm a reduction in inter-cluster and end-to-end delays, a decrease in critical-flow delays, improved load distribution uniformity, and shorter convergence time after topology changes. At the same time, the method increases the frequency of route reconfigurations and the computational cost of the optimisation cycle, and degrades the performance of low-priority flows, which is interpreted as a controlled compromise inherent to multi-criteria optimisation. Conclusions. The proposed method improves the efficiency of flow distribution in dynamic self-organising networks, providing the greatest benefits for critical flows and convergence after topology changes. The improvement is achieved at the cost of higher computational overhead and more frequent route reconfigurations, with reduced performance for low-priority flows. The method is suitable for Fog, Edge, and Cloud environments where adaptive real-time decisions are required under topology changes and resource variability.Downloads
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Copyright (c) 2026 Olena Lozko , Volodymyr Lysechko , Illia Syvolovskyi , Vasyl Pastushenko
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