## 6 Conclusions  
This paper provides a comprehensive comparison of four dynamic link weight allocation methods in the context of a flow aggregation mechanism that employs multipath transmission in a software-defined network. The proposed mechanism uses a centrally controlled MPLS label distribution performed by an SDN controller, eliminating the need for traditional signaling protocols. This innovative approach results in a substantial reduction in both the number of entries and the number of signaling messages within the SDN network. The simulations carried out in the study reveal that the proposed mechanism significantly enhances network performance, as evidenced by improvements in throughput and resource utilization. In the evaluation, we used four weight assignment scenarios inspired by queuing systems. Notable enhancements were observed when the linear scenario was used. This aggressive approach results in a significant increase of a 58% data transfer within the network compared to the base scenario. The increase in network traffic can be observed due to the effective implementation of multipath transmission. Furthermore, the proposed mechanism delivers a notable reduction in the number of entries in the switch tables of the core switches. In addition, it uses flow-based traffic measurements to initiate a novel process to compute congestion-free paths for new flows. This approach minimizes signaling overhead and reduces operational costs. The concurrent decrease in flow entries and OpenFlow messages shows the efficient management of traffic within the SDN environment. Several promising directions for future research and development emerge from the findings presented in this study. More sophisticated algorithms can be considered for dynamic weight adaptation and the potential integration of machine learning or predictive analytics can be explored. Conceptualization, G.R. and Z.D. ; methodology, G.R. and Z.D. ; software, G.R. and Z.D. ; validation, G.R. and Z.D. ; formal analysis, G.R., Z.D. and P.C. ; investigation, G.R. and Z.D. ; resources, G.R. and Z.D. ; data curation, G.R. and Z.D. ; writing--original draft preparation, G.R. and Z.D. ; writing--review and editing, G.R. and Z.D. ; visualization, G.R. ; supervision, P.C and Z.D. All authors have read and agreed to the published version of the manuscript. Research project partly supported by the program \"Excellence initiative--research university\" for the AGH University of Krakow. This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University and by the Priority Research Area Digiworld under the program Excellence Initiative-Research University at the Jagiellonian University in Krakow. This research was carried out with the supercomputer \"Deszno' purchased thanks to the financial support of the European Regional Development Fund in the framework of the Polish Innovation Economy Operational Program (contract no. POIG. 02.01.00-12-023/08). This research was also supported in part by PL-Grid Infrastructure. Moreover, this research was supported by the National Research Institute, grant number POIR.04.02.00-00-D008/20-01, on \"National Laboratory for Advanced 5G Research\" (acronym PL-5G) as part of the Measure 4.2 Development of modern research infrastructure of the science sector 2014-2020 financed by the European Regional Development Fund. Not applicable. Dataset available on request from the authors. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: