Identifying Persistent and Recurrent QoE Anomalies for DASH Streaming in the Cloud

Abstract

Quality of Experience (QoE) anomalies widely exist in all types of video services. As video services migrate to the Cloud, unique challenges occur to deploy video services in the Cloud environment. We study the QoE anomalies for users in a video service deployed in a production Cloud CDN. We use a QoE anomaly identification system, QRank, to identify anomalous systems. We consider Cloud CDN servers, Cloud CDN networks, transit networks, user access networks and different types of user devices. Our extensive experiments in production Cloud find several interesting insights about QoE anomalies of video streaming in the Cloud. 91.4% of QoE anomalies are detected on 15.32% of users. These users experience QoE anomalies persistently and recurrently. The Cloud servers and networks seldom cause QoE anomalies. More than 99.98% of QoE anomalies are identified in anomalous systems including the transit networks, the access networks and user devices. We infer that transit networks are the actual bottleneck systems for QoE anomalies in production Cloud. More than 95% of persistent and recurrent QoE anomalies are identified in less than 10 transit networks. We collect latency measurements to anomalous networks and the analysis indicates that the limited capacity in transit networks are the major cause of QoE anomalies. Resulting anomalies impair user QoEs persistently or recurrently. In order to provide good user QoE, the Cloud provider should identify transit networks that may become bottlenecks for high quality video streaming and appropriate peering with Internet Service Providers (ISPs) to bypass these bottlenecks.