Network softwarization has been proposed as an alternative way for computer networking. This environment has opened up a new era in computer networks where innovative solutions are sought. Undoubtedly, network softwarization is a primary driver behind the thriving 5G/6G and Edge/Core systems. In a software network environment, there is a sharp distinction between pictured and physical networks. A pictured network is an instantiation of a real network that exists for a certain period of time over a physical network. A pictured network, which is ad—hoc and volatile, exploits physical resources provided by the physical network. It utilizes tailored transport functions and policies in order to manage and control assured traffic and services.
In the context of network softwarization, innovative approaches and solutions should be proposed for the sake of networks management, control and monitoring of pictured networks. It is highly desirable to consider QoE scores when executing these network operations. This enables to undertake actions that achieve exactly what is required by end-users. However, the measurement of QoE over software network is still in its infancy and needs an extensive exploration and novel instrumentations. Moreover, considering QoE measurement over a software network is an open issue that requires a deep investigation from theoretical and practical perspectives.
This research work aims at providing new solutions and methods in order to enable QoE-aware management of software networks. The first step consists of proposing an architecture for monitoring QoE accounting for softwarized network environment. It should support different kind of services that could be provided over software networks, e.g. VoIP and video streaming. The proposed architecture should incorporate a protocol enabling quick and automatic evolution of the measurement processes and their configuration. The second step consists of providing an optimized instrumentation strategy, which is automatically specified as a function of parameters required by each invoked QoE function, and the enabled actions by the data plane. The third and final step consists of proposing advanced estimators of missing or inaccessible parameters required by a given QoE function. This relies on AI-based inference techniques that process a reduced and easily-accessible dataset in order to extract valuable and insightful metrics leading to an accurate QoE estimation.
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