Estimating client QoE from measured network QoS.

This research is done in the context of the SliceNet project [4] that aims to extend 5G infrastructure with cognitive management of cross-domain, cross-layer network slices [1], with emphasis on Quality of Experience (QoE) for vertical industries. The provisioning of network slices with proper QoE guarantees is seen as one of the key enablers of future 5G-enabled networks. The challenge is to assess the QoE experienced by the vertical application and its users without requiring the applications or the users to measure and report QoE related metrics back to the provider. To address this challenge, we propose a method for deriving application-level QoE from network-level Quality of Service (QoS) measurements, easily accessible by the provider. In particular, we describe a PoC where QoE, perceived by application users, is estimated from low level network monitoring data, by applying cognitive methods. Our main goal is enabling the cloud provider to support the desired E2E QoE-based Service Level Agreements (SLAs), e.g. by monitoring QoS metrics within the provider's domain to optimize resource allocation through provider's actuators. Additional benefit can be achieved by applying the same technique to troubleshoot issues in the provider's infrastructure. In this work, we employed classical statistical methods to assess the relationship between the application-level QoE and the network-level QoS.