This article introduces the concept, implementation and evaluation of Dynamic Traffic Management (DTM). This management mechanism minimizes traffic transfer costs on inter-domain links. It works for volume and 95th percentile tariffs. DTM reacts dynamically to traffic changes on inter-domain links shifting some part of traffic (manageable traffic) from one link to another. The management mechanism works in a distributed way. It requires cooperation between operators, namely Internet Service Providers (ISPs), receiving and generating traffic. Communication between ISPs is realized by SDN controllers (interdomain SDN). DTM has been applied to inter-cloud communication for Data Centers (DCs) located in different ISP domains. Moreover, it is cloud-agnostic, i.e., DTM does not engage Cloud Service Providers (CSPs) into management procedures. Evaluation performed in the testbed shows that DTM is capable of decreasing ISP costs of transit traffic up to 29%.

With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures. This article introduces a meticulously crafted solution designed explicitly for 6G software-defined networks (SDNs). The approach employs deep neural networks for anomaly detection within network traffic, contributing to a more robust security framework. Furthermore, the paper delves into the realm of network monitoring automation by harnessing dynamic telemetry, providing a specialized and forward-looking strategy to tackle the distinctive challenges inherent in SDN environments. In essence, our proposed solution aims to elevate the security and responsiveness of 6G mobile networks. By addressing the intricate challenges posed by next-generation network architectures, it seeks to fortify these networks against emerging threats and dynamically adapt to the evolving landscape of next-generation technology.

This paper is devoted to analysis of QoS parameters of WebRTC traffic presented for centralized videoconferencing system used for collaborative work. This system consists of a videoconference application, built according to the WebRTC architecture, and a telemetric system, built as the IoT environment. Tests were carried out for three locations of the conference bridge: in private cloud (the OpenStack cloud), in public cloud (the AWS cloud) and in local network. Results show, that conference bridge in a cloud is a good solution for the WebRTC, and additional transmission of telemetric data do not affect the QoS parameters of WebRTC's media stream.