In the era of the Internet of Everything, the burst of traffic will bring many problems. Traffic shaping, as a common means to limit burst traffic rate, achieves ''peak and valley reduction'' to smooth the output rate, avoid network congestion, guarantee the quality of service (QoS) and improve the overall network transmission efficiency. This paper first explains the basic concept of traffic shaping, the related algorithms in traffic shaping, and compares the differences between traffic shaping and traffic policing. It then describes the research on traffic shaping in software-defined networks and the results from the combination of the two. This is followed by an introduction to relatively new technology for industry, timesensitive networks, and an exploration of the functions of various types of traffic shapers in time-sensitive networks. This is followed by a description of case studies of traffic shaping in IoT scenarios and an overview of these traffic shaping schemes, summarizing the usefulness of the shaping schemes. Finally, the future of traffic shaping is explored.

In network management, network measuring is crucial. Accurate network measurements can increase network utilization, network management, and the ability to find network problems promptly. With extensive technological advancements, the difficulty for network measurement is not just the growth in users and traffic but also the increasingly difficult technical problems brought on by the network’s design becoming more complicated. In recent years, network feature measurement issues have been extensively solved by the use of ML approaches, which are ideally suited to thorough data analysis and the investigation of complicated network behavior. However, there is yet no favored learning model that can best address the network measurement issue. The problems that ML applications in the field of network measurement must overcome are discussed in this study, along with an analysis of the current characteristics of ML algorithms in network measurement. Finally, network measurement techniques that have been used as ML techniques are examined, and potential advancements in the field are explored and examined.