# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 5 Numerical Analysis
### Discussions and Insights  
Video-on-demand (VOD) service platforms are systems that allow users to access and view video content on demand, rather than at a predetermined time. These platforms are typically accessed through a web browser or a dedicated app, and can include a wide range of content, including movies, TV shows, documentaries, and other types of video programming. Performability modeling and analysis is a crucial aspect of the design and operation of VOD service platforms, as it involves the evaluation of the performance and reliability of the system under different operating conditions. There are several techniques that have been proposed in the literature for the performability modeling and analysis of VOD service platforms, including queueing theory, Markov models, Petri nets, and simulation. One of the key challenges in the performability modeling and analysis of VOD service platforms is the need to account for the dynamic and highly variable nature of the traffic patterns on these systems. VOD service platforms are typically accessed by large numbers of users at different times of the day, and the traffic patterns can vary significantly, depending on the popularity of the content, the time of day, and other factors. This can make it difficult to accurately model the performance of the system under different operating conditions. To address this challenge, researchers have proposed a variety of techniques for modeling and analyzing the performance of VOD service platforms. For example, some researchers have used queueing theory to model the flow of packets or requests through the system, and to evaluate the impact of different traffic patterns on the performance of the system. Others have used Markov models or Petri nets to capture the temporal dependencies and concurrency in the system, and to analyze the reliability and availability of the service. Simulation is also a powerful tool for the performability modeling and analysis of VOD service platforms. It allows researchers to create detailed models of the system and to evaluate the performance of the service under a wide range of scenarios and operating conditions. This can be useful for identifying bottlenecks and for optimizing the design of the service, as well as for anticipating and responding to  
Figure 10: Models comparison according to multiple metrics. changes in the operating environment. Performability modeling and analysis is a crucial aspect of the design and operation of VOD service platforms. By using techniques such as queueing theory, Markov models, Petri nets, and simulation, researchers can evaluate the performance and reliability of these systems under different operating conditions, and identify potential bottlenecks and areas for improvement. Performability modeling and evaluation of a video-on-demand service platform using SPN models can provide valuable insights and perspectives on the performance and reliability of the service. SPN models are particularly well-suited for this purpose, as they can capture the concurrent and asynchronous nature of the service, as well as the probabilistic transitions between states that are often present in real-world systems. One of the key benefits of using SPN models for performability modeling and evaluation is that they allow researchers to explore the behavior of the service under a wide range of scenarios and operating conditions. For example, an SPN model can be used to evaluate the impacts of different traffic patterns, network conditions, and hardware configurations on the performance of the service. This can be useful for identifying bottlenecks and for optimizing the design of the service. Another benefit of using SPN models is that they can provide a detailed and accurate representation of the service, including the interactions and dependencies between different components. This can be useful for identifying potential issues and vulnerabilities in the service, and for proposing solutions to improve the reliability and availability of the service.