new entities and relationships that cover design artifacts of virtualized and SDN-controlled environments. As VNFs are added, moved, and decommissioned, the measurement points must also be defined, moved, and retired at the same rate. In a flat network architecture with multiple VNFs coresident on hosts in each zone supported by an SDN local overlay network, the traditional static approaches to measure resource utilization, collect flows used for troubleshooting, and advanced service analyt- ics are no longer clearly marked. Virtualization of the network infrastructure and dynamic move- ment of VMs also renders traditional physical probes, fiber splitters, and traditional port mirroring functions obsolete, requiring new virtual probe (vProbe) measurement methods for SDN and NFV environments. These NFV and SDN real-time streaming measurements require the ability to process and analyze massive amounts of data streams originating from different sources in the network infra- structure such as the VNFs, cloud infrastructure telemetry sources, SDN controllers, and vProbes. Real-time analytics processing offers network operators new opportunities to determine where cloud capacity must be added, and allows them to quickly respond to service assurance issues. Applying real-time analytics in a closed-loop mechanism enables operators to automate workloads and optimize outcomes. This chapter focuses on the network data and measurements required by algorithms, SDN controllers, and other value-added applications. Also, the focus here is on the needs of data and measurements for traffic and network capacity management. Chapter 15 looks at the data and mea- surements needed for network operation and alarm detection and handling. 16.1 SDN DATA AND MEASUREMENTS To illustrate how data can be measured and used to optimize network utilization, consider Figure 16.1, which depicts the overall architecture of an SDN-enabled backbone network (see Chapter 13). Much of the material presented in this subsection on the SDN network architectu