An overlay architecture for fault diagnosis in video delivery networks

Abstract

Today's service providers of VoD, SDV and IPTV have a pressing need to proactively detect, isolate and fix outages within access networks. Network induced degradations prove to be detrimental for streaming applications. This typically leads to a poor quality of experience (QoE) for subscribers, leading to subscriber churn. Apart from isolating impairments, service providers often fall short of performing a whole host of other activities: performance discovery (e.g., measuring round trip times of TCP/UDP), topology discovery (e.g., part or full connectivity), service usage discovery (e.g., who is using what service), fraud discovery (e.g., illegal usage), and trend discovery (e.g., forecasting user demands and preferences). In this paper, we show how service providers can simply leverage their existing billing infrastructure which snoops into user traffic to create a whole host of the above mentioned services. We propose a hierarchy of exporters, collectors, and ANCON (ANalysis and CONtrol) nodes that can semi-autonomously monitor, detect and isolate impairments within an access network. Exporters gather and disseminate statistics for individual subnets, which are streamed onto collector nodes. Collector nodes aggregate traffic from various exporters, and stream them onto the root of the tree (ANCON). With an even placement of exporters, root cause analysis can now take the granularity of loss rates or delay rates in individual segments or subnets of an access network. As an extension to our architecture, we show that the overlay can support instrumentations of quality evaluation for streaming video. Logical extensions of this architecture can easily accommodate performance discovery, fraud discovery, trend forecasting and service usage discovery.