Correlating User Perception and Measurable Network Properties: Experimenting with QoE

User perception of a networking service is usually very different from the operators’ understanding of service usability. Quality of Experience (QoE) metrics are supposed to describe the service from the end-users’ point of view – although QoE is hard to measure for mass services. Collection and analysis of QoS and SLS (Service Level Specification) properties of networking services are daily tasks of the operators. These metrics, however often provide misleading description of user satisfaction. Our ultimate aim is to find methods and metrics determining QoE by passive measurements on an aggregated network link. In this paper we describe our experimental results on correlating the severity of a network bottleneck and the experienced service quality. During our measurements we have loaded the network with various kinds of service requests and made notes on the perceived quality. We have also captured packet level traffic, and derived metrics based on packet interarrival times, packet size information and packet loss information. This paper briefly presents some of our analysis results. 1 Causes of Quality of Experience Degradation Quality of Experience is measured – or more appropriately: perceived – at the end terminal, by the user. QoE shows similarities to Quality of Service (QoS), it does, however, differ from QoS by various means. QoE is subjective in nature, furthermore, service availability and user access capabilities get more focus in QoE than in QoS. There can be several scenarios where the experienced service quality becomes less than satisfactory. The roughest QoE degradation is the unavailability of a service. This actually happens more regularly than one might expect. Examples of temporal and/or regular service-unavailability include an unsuccessful paying procedure at a webshop, a broken download, or a ”webpage unreachable” message. These cases are very hard to measure from a central site, since continuous application monitoring and data processing is not always feasible. Another type of QoE violation is tied to network QoS, hence it can be correlated with some