## 5 Conclusions  
We analyzed the existing problems in modern communication networks, such as the fact that the main problem of mobile networks is the focus on the coverage area, not on the user, and the inability of the network to adaptively respond to bursts of large amounts of data created by the user. Therefore, the paper proposes to develop a heterogeneous network, which will be user-oriented and allow the network operator to analyze and predict user behavior through the use of cloud technologies.  
In the work, a study of technologies for the effective functioning of heterogeneous mobile networks was carried out. It analyzes the current technology and gives an opportunity to improve the quality of service. On the basis of the data received as a result of the study, the model for improving the performance of a heterogeneous network using a big data processing system was developed. The comparison of criteria for effective resource management was carried out and the choice of the criterion of maximum uniform loading of the mobile network was made.  
We developed a comprehensive method for flexible resource management in a heterogeneous network, including statistical resource reservation for a certain type of service in each technology, dynamic resource reservation, and user priority analysis, which can reduce the number of dissatisfied customers.  
We proposed the use of big data technologies for optimal resource management in mobile networks. For effective data analysis in a heterogeneous system, we classified the data into two types: user data and network operators' data. The analysis of both types of data allows us to isolate valuable information that is used for network optimization and flexible resource management.  
As a practical result, we implemented a big data processing system. For its implementation, we used the cloud computing service DigitalOcean in which we created an account and configured a virtual server for data processing. We used the technology of virtualization at the operating system level with the help of the Docker platform, which allowed us to create two separate virtual containers, where we deployed non-relational database Apache Cassandra and the platform for fast real-time data analysis, Apache Spark. Scripts were developed in the Python programming language to analyze large amounts of data and, by filtering and sequencing, to output the correct data, allowing intelligent decisions to be made for resource management and predicting the behavior of a heterogeneous network.  
Figure 20: Diagram of heterogeneous network functioning with estimation of user priority.  
We evaluated the effectiveness of the proposed solutions and achieved a 16% increase in performance of the heterogeneous network by using the statistical network resource reservation method, compared to homogeneous networks, and a 13% increase by using uniform resource allocation and a dynamic reservation process, compared to the previous method. Through user prioritization, the quality of user service in the heterogeneous network is improved and the number of dissatisfied customers is reduced.  
All authors contributed to the study conception and design. Methodology, M.B. and H.B. Formal analysis and investigation, O.Y. and H.B. Writing of the original draft preparation N.K. and H.B. Writing of review and editing, M.B. and N.K. Funding acquisition N.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ukrainian project No. 0120U100674 \"Development of the novel decentralized mobile network based on blockchain-architecture and artificial intelligence for 5G/6G development in Ukraine\". This work was supported by the Ukrainian project No. 0120U102201 \"Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks\", and by the Faculty of Management of Comenius University in Bratislava, Slovakia.  
Not applicable.  
The authors declare no conflict of interest.