# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
###### Abstract
It is a variant of the SDF model that allows for the creation of feedback loops, which are not possible in SDF. In SRDF, tasks can consume tokens from their own output channels, allowing them to execute in an iterative manner. (iii) _SADF_: SADF stands for Stochastic Activity Dataflow. It is a dataflow model used to describe systems in which the execution of tasks is probabilistic, rather than deterministic, as in SDF and SRDF. In an SADF system, each task has a probability distribution associated with it, which determines the likelihood of the task being executed in a given iteration. All three of these dataflow models can be used to model concurrent systems, and they are often used in the design and analysis of real-time systems, such as control systems and communication networks. Dataflow models, such as SDF, SRDF, and SADF, and stochastic Petri nets (SPNs) are both formalisms used to model concurrent systems. Both types of models have their own advantages and disadvantages, depending on the specific context in which they are used. Here are a few potential disadvantages of dataflow models compared to SPN. _(i) Dataflow models may be less expressive than SPN:_ Dataflow models are designed to describe systems in which data is exchanged between tasks through channels. They are primarily used to model systems with a fixed, predetermined order of execution, and do not explicitly represent the state of the system. In contrast, SPN can model systems with more complex behavior, including the ability to change state and to perform probabilistic transitions. _(ii) Dataflow models may be less flexible than SPN:_ Because dataflow models are designed to model systems with a fixed order of execution, they may be less flexible than SPN when it comes to modeling systems that exhibit more complex or dynamic behavior. For example, it may be more difficult to model systems with feedback loops or systems that exhibit non-deterministic behavior using dataflow models. _(iii) Dataflow models may be more difficult to analyze than SPN:_ Dataflow models may be more difficult to analyze than SPN because they do not explicitly represent the state of the system. This can make it more challenging to understand the behavior of the system and to identify potential problems or bottlenecks. In contrast, the SPN can be analyzed using a variety of techniques, such as reachability analysis and model checking, which can provide more detailed information about the behavior of the system. It is important to note that these are just a few of the potential disadvantages of dataflow models compared to SPN, and that the relative usefulness of each type of model will depend on the specific requirements of the system being modeled. Real-time calculus is a mathematical framework for analyzing and predicting the behaviors of real-time systems, which are systems that operate in real-time, with strict timing constraints. Real-time calculus is more suitable for analyzing systems with strict timing constraints, whereas SPNs are more general and can be used to model a wider range of systems. Thus, the choice of which modeling technique to use depends on the specific characteristics and requirements of the system being modeled. SPNs are a useful tool for modeling and analyzing probabilistic and concurrent systems, but they may not be the best choice for all situations. This paper depicts the streaming service's context and focuses on availability analysis. [PERSON] et al. [8] is the only study connected to this that does a sensitivity analysis and suggests in its paper the use of Reliability Block Diagram (RBD) models to reach the findings. Unlike our research, [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] et al. [11], evaluate the quality of service of a streaming service through the variation of the capacity. Unlike the others, [PERSON] et al. [12] chose to work not only in video-on-demand, but in the general context of streaming.