# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 6 Conclusions  
This research concludes by answering and researching multiple questions about quality of experience and its prediction. Firstly, we aimed to ease the replication of this research by building a new simulation environment for future researchers in similar fields. This simulation is able to run both P4 and Openflow, along with Python 2 and 3 instances, DASH and Mininet. With all essential packages installed, an error-free test environment for running this and other related experiments and applying previous and similar work to compare and contrast results is installed. Furthermore with this build, a P4 SDN test bed over Mininet was designed with the ability to control DASH initial buffering, stalling, switching, monitoring, bit rate adaptation and bandwidth limitation over selected ports. The test bed provides the capacity for comprehensive user experiments and data collections, which lead to our insights and analysis of congestion for congestion-related experiments, along with full re-configurability over data plane. This being an open-source project clears the difficulties and eases the research methodology into media streaming applications, tests and experiments. Was it possible to create a hybrid environment for testing on a user level and predicting with machine learning with simply inserting the network or user feedback data to generate the other? With this open-source project, we cleared that hurdle. Moreover, we proposed an experimentation framework structure through programmable network management for the generation of ML training-ready data and MOS/QoE prediction with the aid of a human experiment with QoE MOS-based feedback to benchmark the accuracy of the predicted QoE and network features. Our analysis of state-of-the-art machine learning algorithms, along with the creation of our framework for feature evaluation in network experiments is unique in its attribute and network design; however, it is reprogrammable to match any type of media experimentation. Understanding user-effective network data has become as crucial as the QoE on individual user devices. This paper discusses different features of network-level experimentation and the prediction of QoE in multimedia applications. We also discussed how perceivable QoE is linked to resource allocation and traffic engineering at the network level and how emerging programmable networks such as SDN can be used as a tool to improve user feedback and how that data can be used to predict human feedback. An automated data collection and prediction framework is also proposed to harness the capabilities of new network designs and growing availability of computing resources in future networks for fairness-aware content distribution. As shown in Table 8, the ML classification methods are used and compared for the features that are used for prediction. We conclude that our generated dataset and experimentation framework can support the development of a high-accuracy machine learning model for QoE estimation. The use of existing neural networks also proved effective with our data but consumed more computational power and time as mentioned in the previous section. Our future work will look into implementations of this QoE/MOS predictor in a live smart home environment. Conceptualization, A.O.B.A.-M.; methodology, A.O.B.A.-M.; software, A.O.B.A.-M.; validation, A.O.B.A.-M.; formal analysis, A.O.B.A.-M.; investigation, A.O.B.A.-M.; resources, A.O.B.A.-M.; data curation, A.O.B.A.-M.; writing--original draft preparation, A.O.B.A.-M.; writing--review and editing, A.O.B.A.-M.; visualization, A.O.B.A.-M.; supervision, M.M. and A.A.-S.; project administration, A.O.B.A.-M.; funding acquisition, A.O.B.A.-M. All authors have read and agreed to the published version of the manuscript. This work is supported by UK Research and Innovation (UKRI) under EPSRC Grant EP/P033202/1(SDCN).