# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
###### Abstract  
In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of \"o s\" from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models. streaming; video-on-demand; Petri nets; SPN; performability evaluation +
Footnote †: journal: Applied Sciences  
1 PASID Lab, Federal University of Piaau (UFPI), Picos 64600-000, Brazil 1  
School of Software, College of Computer Science, Koolmin University, Seoul 02707, Republic of Korea 1  
Konkuk Aerospace Design-Airworthiness Institute (KADA), Konkuk University, Seoul 05029, Republic of Korea 1  
individuals spent an average of two hours per day consuming information on streaming platforms. According to [5], Netflix ended the first quarter of 2020 with 15.77 million new members. Other companies, such as YouTube, Amazon Prime Video, HBO MAX, and the newcomer Disney+, also operate in this multi-million-dollar industry, which is expected to continue growing in the future. Netflix videos often pass through Internet Service Provider (ISP) data centers before reaching consumers' homes. This type of Netflix service is known as the Open Connect program ([[https://openconnect.netflix.com/en/](https://openconnect.netflix.com/en/)]([https://openconnect.netflix.com/en/](https://openconnect.netflix.com/en/)), access on 20 October 2022). The Netflix Open Connect program is a content delivery network (CDN) created by Netflix in 2011 that has continued to grow over the years. This CDN allows physical servers located near users to improve the user experience by reducing delivery times and by causing minimal additional Internet traffic. These networks are called ISPs (Internet Service Providers) and are responsible for approximately 90% of Netflix's total traffic. The initiative is divided into two parts: Open Connect Appliances (OCAs) and Settlement-free connections (SFIs). Both components were designed in collaboration with ISPs to maximize the benefits in each situation. Open Connect Appliances can be integrated into the ISP network, increasing the speed of data flow in the provider's service areas. Multiple physical installations can be dispersed or aggregated geographically or network-wide to maximize local capacity.