smission. With the advent of multimedia and the delivery of large amounts of content, it has become harder to manage network per- formance with a view to achieving a certain QoS as experienced by the end-user. Service providers cannot ignore end-to-end customer QoS because it is directly related to customer satisfaction and customer churn. Installing test units for each serv- ice at each customer site would give complete visibility but would be costly and hard to manage. More realistic is to use network monitoring hardware and software to collect network performance data. Different aspects of services critically rely on various net- work performance parameters, and so correlation and trend analysis methods can be used to estimate end-user QoS. In mobile networks, each call generates a large amount of QoS data and so correlation methods are most advanced in mobile networks. Performance data collected at lower network levels can be cascaded up to the cus- tomer layer by correlating lower level network events with relationships between the network and services. To avoid provisioning services that are not supported by resources, the service provider uses QoS metrics that apply across network layers. A typical optical network has at least three interdependent layers: 1. fibre-optic cables at the pure physical layer 2. optical channels on another physical layer 3. data transmission protocol [such as synchronous optical network (SONET), Ethernet, IP] on another layer. OSS for Telecom Networks customer layer service level metrics service management layer network level metrics optical layer network level metrics physical layer Figure 6.1 Correlation of performance of lower network layers with QoS at the higher customer layer. The third layer is subdivided below the customer layer. Each layer is dependent upon the layers beneath it. If the SONET overhead indicates bit errors in a path on the data transmission layer, packets can be lost which would affect real-time appli- cations. Often there is an explosion in the effect o