A signalization in an Internet protocol environment is commonly used for monitoring quality of service and other parameters of a network. This thesis is involved in transmission of signalization through internet protocol networks and proposes scalable solution for small and even for large-scale internet television broadcasting. The main contribution of this thesis lies in design and validation of optimal hierarchical tree on the basis of resources assigned. This is done in respect to geographical distance, network distance of each particular member of the hierarchical structure. For the design of algorithms simulations and global experimental network were used.

Internet Protocol Television (IPTV) transmits the television signal over the TCP/IP family of protocols. Its advantages are for example that the transmitting is not only one-way as in the “classical” TV broadcasting, but it can provide feedback such as interactivity. There are also some problems which avoids development, for example low channel capacity of access networks. That is why new methods , for example how to get more efficiency in IPTV transmission, are proposed. The main task of this diploma thesis is to visualize tree structure of relations between nodes in the network, based on understanding of principles of the hierarchical summarization and IPTV transmitting. The nodes in the tree structure provide computing and summarizing of the data in back-way channel. There is the data from the end users in this channel. In the first part of this diploma thesis there is explained the principle of IPTV and its differences as compared with classical TV broadcasting. The part is also aimed for the supported services, advantages and disadvantages. There is explained the compressing data with the MPEG-2 and MPEG-4 standards and problems with transport networks called “last mile problem.” To transmitting data IPTV uses Source Specific Multicast – every user connects to the multicast session with requested TV program. Feedback is provided by unicast. Feedback network uses the hierarchical summarization principle to reduce the data. This problem, connected with RTP, RTCP and TTP protocols, is in the work described as well. There is an international experimental network called PlanetLab mentioned in theoretical part of this work. Proposed structure of new protocol and applications including the visualization for IPTV broadcast, is tested in that network. In the practical part of this work there are discussed possibilities and methods for the visualization and data storage. Because of high availability and flexibility, there were chosen web technologies, such as MySQL for data storage. The tree model is executed by Java. The visualization is solved by web technologies, source code for visualization is dynamically generated by scripts in JSP (Java Server Pages). Graphical output is provided by the vector format SVG (Scalable Vector Graphics) which is created for graphical expression on the internet and in the mobile phones. There were created interactive web application thanks its ability to cooperation with Javascript technology. This application visualizes relation-tree structure of nodes. In this work there are explained basics of all used technologies, there are also given reasons for chosen methods and formats. Examples and interesting parts of solution are mentioned as well.

Deep Neural Networks (DNNs) find widespread applications across various domains, including computer vision, recommendation systems, and natural language processing. Despite their versatility, training DNNs can be a time-consuming process, and accommodating large models and datasets on a single machine is often impractical. To tackle these challenges, distributed deep learning (DDL) training workloads have gained increasing significance. However, DDL training introduces synchronization requirements among nodes, and the mini-batch stochastic gradient descent algorithm heavily burdens network connections. This dissertation proposes, analyzes, and evaluates three solutions addressing the communication bottleneck in DDL learning workloads. The first solution, SwitchML, introduces an in-network aggregation (INA) primitive that accelerates DDL workloads. By aggregating model updates from multiple workers within the network, SwitchML reduces the volume of exchanged data. This approach, which incorporates switch processing, end-host protocols, and Deep Learning frameworks, enhances training speed by up to 5.5 times for real-world benchmark models. The second solution, OmniReduce, is an efficient streaming aggregation system designed for sparse collective communication. It optimizes performance for parallel computing applications, such as distributed training of large-scale recommendation systems and natural language processing models. OmniReduce achieves maximum effective bandwidth utilization by transmitting only nonzero data blocks and leveraging fine-grained parallelization and pipelining. Compared to state-of-the-art TCP/IP and RDMA network solutions, OmniReduce outperforms them by 3.5 to 16 times, delivering significantly better performance for network-bottlenecked DNNs, even at 100 Gbps. The third solution, CoInNetFlow, addresses congestion in shared data centers, where multiple DNN training jobs compete for bandwidth on the same node. The study explores the feasibility of coflow scheduling methods in hierarchical and multi-tenant in-network aggregation communication patterns. CoInNetFlow presents an innovative utilization of the Sincronia priority assignment algorithm. Through packet-level DDL job simulation, the research demonstrates that appropriate weighting functions, transport layer priority scheduling, and gradient compression on low-priority tensors can significantly improve the median Job Completion Time Inflation by over $70\%$. Collectively, this dissertation contributes to mitigating the network communication bottleneck in distributed deep learning. The proposed solutions can enhance the efficiency and speed of distributed deep learning systems, ultimately improving the performance of DNN training across various domains.

The work deals with the measurement and management in power. Provides an introduction to the problems of remote meter reading, management, and describes the current situation in the field of modern technologies Smart metering and Smart grids. It also analyzed the issue of collection of networks and data collection from a large number of meters over a wide area. For the purpose of data transmission are described GPRS, PLC, DSL, ... Further, there are given options to streamline communication. This area is used hierarchical aggregation. Using k-means algorithm is a program designed to calculate the number of concentrators and their location in the group of meters. The finished program is written in Java. It has a graphical interface and shows how the calculation is conducted. To verify the results of the optimization program is given simulation model in OPNET Modeler tool. Audited results are described in the conclusion and can deduce that using the optimization program is to streamline communications.