# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
###### Abstract
These metrics are typically used to measure the performance of the network and the services provided by the ISP, and they can help the ISP to identify areas for improvement and optimize their operations. SPNs are a subclass of Petri nets that can be used to model and analyze systems with concurrent and asynchronous events, such as video streaming services. They are particularly useful for analyzing systems with probabilistic transitions between states, as they can capture the uncertainties and randomness that are often present in real-world systems. SPNs can be used to model the flow of packets or requests through a video streaming service, as well as the dependencies between different events. For example, an SPN model of a video streaming service might include transitions for requesting a video, buffering the video, and playing the video, as well as places to represent the state of the system at each stage of the process. The SPN model can then be used to analyze the performance of the video streaming service under different operating conditions, such as varying traffic patterns and network conditions. There are several techniques that can be used to evaluate the performance of a video streaming service using an SPN model. One common technique is to compute the throughput and response time of the system, which can be used to measure the efficiency and effectiveness of the service. Another technique is to compute the reliability and availability of the service, which can be used to assess the robustness and dependability of the service. In addition to these techniques, SPN models can also be used to analyze the impacts of different hardware configurations and network conditions on the performance of a video streaming service. For example, an SPN model can be used to evaluate the impact of different server configurations or network architectures on the reliability and availability of the service. This can be useful for identifying bottlenecks and optimizing the design of the service. Overall, SPN models are a powerful tool for the performability modeling and analysis of video streaming services. They allow researchers to capture the complexity and uncertainty of real-world systems and to evaluate the performance of the service under different operating conditions. SPNs are a type of Petri net that allows for the modeling of probabilistic, concurrent, and discrete systems. They are used for modeling and for analyzing the behaviors of systems that exhibit random or probabilistic behavior, and for predicting the performances of such systems. One advantage of SPNs is that they can represent the probabilistic behavior of systems more accurately than other models, such as deterministic Petri nets or finite state automata. This is because SPNs allow for the specification of probabilities or rates that are associated with transitions between states, which can be used to model the inherent randomness or uncertainty present in many real-world systems. SPNs also have the advantage of being able to model concurrent systems, in which multiple processes or activities can occur simultaneously. This is in contrast to models such as finite state automata, which can only model sequential systems. In terms of a comparison with other models, dataflow models such as SDF, SRDF, and SADF are primarily used for modeling and for analyzing the behaviors of digital systems, such as computer hardware and software. In the context of dataflow models, SDF, SRDF, and SADF are all formalisms used to describe the behaviors of concurrent systems. Here is a brief overview of each of these dataflow model formalisms: (i) _SDF_: SDF stands for Synchronous Dataflow. It is a model of computation that is used to describe systems in which the execution of tasks is synchronized by the exchange of data through channels. In an SDF system, tasks are executed in a fixed sequence, and each task consumes and produces a fixed number of tokens from and to its input and output channels, respectively. (ii) _SRDF_: SRDF stands for Static Dataflow.