# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 7 Conclusions  
In this work, four SPN models (one base model and three extended models) were proposed for evaluating the performability of a video-on-demand system. These models were used to demonstrate and to analyze the performance challenges of a video-on-demand architecture. The extended models were constructed based on the results of a sensitivity analysis, using the Design of Experiments (DoE) approach, which 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 influential components. This redundancy increased the number of \"9 s\" of availability from three to five. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **Metrics** & **Capacity Variation** & **Sensitivity Analysis** & **Multiple Redundancy Strategies** \\ \hline
[37] & Population, peer uploads, and bandwidth demand & & & & \\ \hline
[38] & CDF, transient times, and factor of safety & & & & \\ \hline
[39] & Bit rate & & & & \\ \hline
[12] & A 7-layer architecture diagram & & & & \\ \hline
[40] & Drop rate, utilization, arrival rate, No. of requests, and QLRA & & & & \\ \hline
[28] & Queuing algorithms (DropTail, BLUE, and CoDel) & & & & \\ \hline
[9; 10; 11; 29; 41] & Quality of experience (QoE) and Quality of service (QoS) & & & & \\ \hline
[30] & Drop probability, Number of contacts during association, Server stress, and number of contacts during recovery & & & & \\ \hline
[31] & Startup latency, fetch latency, and jitter & & & & \\ \hline
[32] & Traffic Metrics, Congestion Metrics, traffic, balance, user satisfaction, and cache replacement & & & & \\ \hline
[8] & Reliability and Availability & & & & \\ \hline
[33; 34] & Reliability and Availability (MTTF, MTTR) & & & & \\ \hline
[35] & Dependability & & & & \\ \hline
[36] & Availability & & & & \\ \hline
[42] & Availability & & & & \\ \hline
**This Work** & Performance and availability & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Related Works. One of the most effective redundancy strategies was Hot-Standby. Unlike the Cold- and Warm-standby redundancies, the secondary machine is represented in the Hot model by simply adding a token to the component. This improvement improves the availability, because when the first computer is shut off, the secondary machine immediately offers resources, allowing the services to be available at all times. We can achieve better outcomes since both devices are fully operating and ready to take over if one fails. In future studies, we plan to increase the complexity of the models by introducing more potential failure points. We also plan to enhance the functionality of the Back-End component, and to evaluate the concept in a real ISP. Probable failure refers to the likelihood that a system or component will fail or malfunction. In the context of stochastic Petri nets, the probable failure can be represented by introducing additional places to the net that represent the states of the system or component. To introduce more places to a stochastic Petri net to capture probable failures, the following steps can be considered: (i) Identify the states that represent probable failures in the system or component. These could include states such as \"failure\" or \"malfunction\"; (ii) Add these states as additional places in the Petri net; (iii) Add transitions between these places and the other places in the Petri net to represent the flow of the system or component between these states; (iv) Assign probabilities to these transitions to represent the likelihood of the system or component transitioning between the different states. By adding these additional places and transitions to the Petri net, we can model the probable failures of the system or component and analyze their impacts on the overall system.