tunities for virtual probes will enable faster and more accurate collection of mea- surements in more places. 16.3.4 TRAFFIC FORECAST It is common practice for network operators to model their traffic and derive traffic forecasts for the future. The traffic forecast is then used as input to capacity planning to determine how to aug- ment the network, for example, installing additional regenerators, transponders, router ports, and establishing new wavelength channels among specific routers. A fundamental approach for traffic forecast is to study historical traffic measurement to quantify the trend of growth. Based on the quantified growth rate and the current traffic load, an estimated future traffic load is then derived. A critical question is how to improve forecast accuracy. A conservative forecast could lead insufficient capacity augmentation, which in turn results in network congestion and degraded qual- ity of service (QoS). An aggressive forecast would cause subsequent capacity planning to add exces- sive resources to the network and thus incur unnecessary cost. In practice, it is a difficult challenge to obtain an accurate traffic forecast because the network carries heterogeneous services that could have very different usage patterns and growth rate. Some traffic may peak during the day and some may peak during the night. Some traffic may cross the entire backbone network and some, being largely localized, may span just a couple of hops. Some services may have stable volume and some may grow rapidly. In a pre-SDN environment, the only viable approach to improving forecast accuracy was to attempt fine-grained modeling. Given the service heterogeneity, it helps to take a fine-grained approach to classify traffic into different types and model and forecast each type of traffic separately. Network Measurements For example, media streaming services could be a significant percentage of the total traffic and they often demonstrate large variation and high growth rate. Such services could be highly localized in t