# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 6 Related Works
Device capabilities, such as processing power and Internet connection speed, can also affect the performance of the video stream. Another study found that the use of adaptive bitrate streaming (ABR) can improve the performances of video streaming services. ABR is a technique that adjusts the bitrate of the video stream in real-time, based on the user's device and network conditions. By using ABR, video streaming services can provide a more stable and consistent viewing experience for the user. A third study found that the reliability of video streaming services is also influenced by the type of content being streamed. Live events, such as sports and concerts, are more susceptible to quality issues due to the real-time nature of the content. On-demand content, such as movies and TV shows, is generally more reliable as it is pre-recorded and can be stored on the streaming service's servers. So, the performability of video streaming services is impacted by a variety of factors, including network congestion, CDN performance, device capabilities, and the type of content being streamed. The use of techniques such as ABR can help improve the performance and reliability of these services, and enhance the user experience. Furthermore, there is no universal model that currently provides a scalable and adaptable architecture for local streaming processing. This study stands out as being noteworthy because it depicts a model of a video-stream topology that designers may employ in ISPs. Most of the research concentrated on video streaming because it is more relevant to the topic of this article. On the other hand, [PERSON] et al. [12] opted to focus on the streaming service in general, offering a seven-layer architecture made up of micro-services. The study demonstrates that the design achieves a good mix of scalability and maintenance due to the solution's strong cohesion and minimal coupling, with asynchronous communication across layers. [PERSON] et al. [8] calculate the availability of streaming video services in a lab-based test environment. The authors conducted a sensitivity analysis to identify the system's crucial points in terms of availability so that they may be addressed, and to increase the system's availability. Among the selected research, this was the only one that performed a sensitivity analysis. As previously stated, there is no standard for this analysis. Some metrics are comparable to those used in our article, in that they analyze the quality of service (QoS) results using queuing networks. [PERSON] et al. [28] have a great advantage in presenting MRT metrics, utilization, number of requests, discard rate, and availability. However, [PERSON] et al. [28] did not explore multiple redundancy proposals to increase system availability. On the other hand, only two works presented capacity evaluation in the proposed architectures ([9,28]). [PERSON] et al. [9] use the evaluation of the quality of experience (QoE), but different from our research, it evaluated the variation in the capacity of the service provided. [PERSON] et al. [10] use a different approach, with a subjective assessment of video quality involving human observers. This technique allows for evaluating audiovisual quality in the same places and situations in which viewers often watch television. Much is debated in the scientific community about QoE; one example is the obtaining of data from an industrial video streaming test bench to utilize in an experiment. [PERSON] et al. [11] use this methodology in their work, where a method to predict QoE is proposed. [PERSON] et al. [29] focus on video encoding standards, diversified bitrate adaptation techniques, and parameters such as bandwidth, throughput, packet loss, delay, or jitter. The demand streaming approach must handle asynchronous peer arrivals quickly and enable strong recovery in the face of frequent peer failures. [PERSON] et al. [30] propose an answer to this challenge with a multicast tree of applications called P2 VoD (peer-to-peer for streaming video-on-demand).