hm several higher priority traffic ahead of lower priority ones without times (can take many hours) to identify the best significantly increasing the run time candidates for adding new IP/optical links Can react to fast traffic pattern changes and topology changes (during traffic surges/failure events) or removing (~seconds) IP/optical links (after the event is over) It is feasible to run the algorithm several times (takes several A network capacity planning exercise requires seconds) to identify the best candidates for adding new IP/ running the algorithm hundreds of times optical links (during traffic surges/failure events) or removing simulating many failure scenarios and IP/optical links (after the event is over) time-of-day traffic variations. This will take It is feasible to do network capacity planning exercise since several days with the multicommodity flow even running the algorithm hundreds of times would take a approach and SO is not feasible few minutes 13.3.3 OPTIMIZATION ALGORITHM IMPLEMENTATION The optimal bin packing can be done by solving a multicommodity flow problem using linear pro- gramming. However, this approach is slow and is not suitable for repeated application under chang- ing traffic patterns and topology changes under unpredictable failures. Furthermore, it is also not suitable for short-term capacity engineering (optimal choice of adding/deleting resources among an available pool) or long-term capacity planning (deciding where to keep a pool of resources SO that it can be used optimally over many different failure scenarios). AT&T has developed a fast heuristic algorithm that runs in subseconds to seconds and SO is suitable to respond to fast traffic/topology changes and also for short- and long-term capacity engi- neering. Furthermore, its efficiency is 99% or better compared to the true optimal algorithm (using multicommodity flow). Table 13.2 explains the key benefits of the fast heuristic algorithm compared to the multicommodity flow approach. ACKNOWLEDGMENTS The author would lik