to significant concentration of traffic in certain regions of the network, the link capacities are usually not the same and have significant variation. We note that even though the maximum traffic in the dominant direction is close to the capacity, there is significant bandwidth left in the other direction. Furthermore, even the dominant direction gets close to the capacity only during peak hour and there may be significant spare capacity during off-peak hours. Based on accurate and up-to-date traffic measurements available at the SDN controller and fore- casting, it is possible to offer bandwidth calendaring service that can use the spare capacity oppor- tunistically. For the same example, Figure 16.5 shows the spare sellable traffic among a set of data centers as a function of time of day and using a normalized scale. The "typical" case assumes that the demand for bandwidth calendaring is symmetric among the data centers and independent of where capacity is available while the "maximum" case assumes that the bandwidth calendaring demand matches closely with the available spare capacity. It is observed that there is some band- width calendaring opportunity during the network peak period and significantly higher opportunity during off-peak hours. Building the Network of the Future Max_Traff_Dominant Max_Traff_Other Capacity IP Inter-City Links (Largest to Smallest) FIGURE 16.4 Maximum traffic versus capacity of IP link bundles. Typical Off-Peak Maximum Time in Units of 5 Minutes FIGURE 16.5 Spare sellable traffic among a set of data centers. 16.3 NETWORK CAPACITY PLANNING The previous section demonstrates how the real-time network data can help drastically improve traffic management of the transport network with minimal network resources and without sacrific- ing high level of network reliability. In this section, we will discuss how SDN can also transform the way we address various capacity planning activities. Again, SDN transformation allows timely information, additional intelligence, process integration, and