me approximation is in any case used to make the problem solvable. For a discussion on different joined optimization methods, see Refs. [6,8,9]. 8.4.2.3 ILP Design Complexity The ILP optimization model we have presented in the last section is very explicit in repre- senting the capacity routed on every link, which is useful in those cases in which DWDM links have to be designed after the network general optimization. Other model exists that are a bit less complex from a computational point of view [16-18], however, whatever model is defined, the optimization of a multilayer mesh net- work is an NP hard problem, which means that the problem nature implies an exponen- tial complexity increase with the key scale variable, which in our case is the number of nodes. Thus it is important to understand how the computation time can be shortened and what does it mean in terms of accuracy of the found solution. A very good analysis on this point is reported in Ref. [6] and we encourage the reader interested to detailed results on specific examples to start from this reference. Here we will cite and justify Convergent Network Management and Control Plane intuitively a few techniques for ILP complexity reduction without reporting a detailed quantitative analysis. The first observation is that ILP goes uniformly toward the optimum solution, thus there is a relevant part of the time used by the algorithm to refine an already good solution. There is a measure of how much the configuration found in an intermediate state is far from the final optimization, which is the so-called solution optimality gap to be evaluated comparing the fully optimized solution with the intermediate one and measuring the cost difference in percentage with respect to the optimum cost. It has been demonstrated in Ref. [6] a quite general result regarding algorithms like this: if an optimality gap of the order of 3% is accepted the computation time can be signifi- cantly shortened and even quite large networks can be analyzed. In our case, trying to find 