Multi-Layer QoE Learning System Implemented by Fiberhost

Abstract

Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most network mechanisms, protocols and applications feature adaptations that allow them to change the parameters of the transmission channel and try to minimize the negative impact of the network on the perceived quality, for example by temporarily changing the modulation scheme, or coding scheme, or by re-transmitting lost packets, or buffering to compensate for the interruptions in transmission. To respond appropriately, network operators are interested in knowing how well these adaptations are performing in order to assess the ultimate quality of their networks from the user perspective, i.e., Quality of Experience (QoE). Due to the huge amount of data associated with the collection of various parameters of the telecommunications network, machine learning methods are often needed to discover the relationships between various parameters and to identify the root cause of the observed network quality. In this paper, we present a Multi-layer QoE learning system implemented by Fiberhost for QoE analysis with a multi-layer approach based on machine learning tools.