## 3 Experiment Workflow
### Traffic Acquisition and Its Characteristics
From different network parameters set by the authors, we used the traces which resembled our network parameters (the bandwidth, packet delay, and jitter) when capturing the non-video traffic. Video streaming services have widely adopted different implementations of DASH. DASH establishes a standard for delivering video over HTTP, to improve the quality of experience (QoE) and optimise bandwidth usage. A video uploaded to the server is encoded into multiple files of varying qualities (e.g., 480 p, 720 p, and 1080 p). Then, the server splits every file into segments of fixed time, usually between 2 and 10 s. Additionally, the server generates an media presentation description (MPD) file, which contains video metadata, such as information about its quality and segmentation. When a user attempts to view the video, the video player first retrieves this MPD file and then requests video segments as needed. During streaming, the video player selects the quality of the next segments based on network conditions parameters such as bandwidth, delay, or jitter. The video download has two phases. The video stream begins with buffering, followed by a steady-state phase. In the steady-state phase, there is a cycle of ON/OFF periods, which is used to limit the download rate. The DASH standard specifies only an abstract mechanism, enabling streaming service providers to implement various streaming technologies while adhering to the same standard. Therefore, video providers design their streaming strategies to be resilient to different network conditions. Usually, the volume of data that a video player downloads in the buffering and steady-state phases is different. For example, YouTube clients can request combinations of multiple video segments, which produces variable traffic burst sizes and time intervals, resulting in multiple traffic patterns when streaming the same video [23]. The traces listed in Table 1 are varied, and each of them possesses unique statistical characteristics. In the case of file download, which is presented in Figure 3a, the trace nearly saturates the link and absorbs its available bandwidth. From time to time, the throughput drops rapidly, which is probably due to the system update mechanism. Namely, the update consists of many files downloaded sequentially. When an OS downloads a certain number of files, it verifies them and checks their integrity, which results in several-second pauses in downloading. The gaming traffic absorbs less network bandwidth, but its variability is higher than the file download process (see Figure 3b). Its amplitude is similar to the traffic amplitude generated by Facebook browsing, which is presented in Figure 3c, but the latter has more breaks. These are probably the result of user behaviour, as the user creates a break by consuming content while scrolling through a Facebook webpage. In the case of gaming, a user has less influence on breaks; it is the gameplay that forces breaks on the user. The individual traces generated by video streaming are also varied. YouTube and Netflix traffic both demonstrate an ON/OFF structure, where traffic is generated during ON periods, and, during the OFF periods, there is no data transmission. However, the period duration is significantly longer for YouTube than for Netflix. The traffic generation process for Amazon's Prime Video also demonstrates two states, in which traffic is generated with greater or lesser intensity. According to [4], YouTube (Figure 3d) and Netflix (Figure 3e) have longer ON intervals. In the results, we can see that a larger volume of data is downloaded at a time, in comparison to Prime Video (Figure 3f). However, even though Prime Video's cycle size is small, it downloads an equal amount of data in the same period by having a greater number of cycles.