The general objective of the RWA problem is to maximize the number of established connections.
Two connections requests can share the same optical link, provided a different wavelength is used.
The RWA problem can be formally defined in an integer linear program (ILP).
In other words, solving the SLE RWA problem is as complex as finding the chromatic number of a general graph.
Many of the optical impairments are nonlinear, so a standard shortest path algorithm can't be used to solve them optimally even if we know the exact state of the network.
This is usually not a safe assumption, so solutions need to be efficient using only limited network information.
Given the complexity of RWA, there are two general methodologies for solving the problem: Fixed path routing is the simplest approach to finding a lightpath.
This algorithm calculates the shortest path using the number of optical routers as the cost function.
The SP-1 algorithm could be extended to use different cost functions, such as the number of EDFAs.
shortest paths using the number of optical routers as the cost function.
For these reasons, most of the research in RWA is currently taking place in Adaptive algorithms.
Five examples of Adaptive Routing are LORA, PABR, IA-BF, IA-FF, and AQoS.
Adaptive algorithms fall into two categories: traditional and physically aware.
This requires each optical switch to broadcast recent usage information periodically.
The physically aware backward reservation algorithm (PABR) is an extension of LORA.
The nonlinear impairments, on the other hand, would not be possible to estimate in a distributed environment due to their requirement of global traffic knowledge.
PABR also considers signal quality when making the wavelength selection.
It accomplishes this by removing from consideration all wavelengths with an unacceptable signal quality level.
With multi-probing, multiple paths are attempted in parallel, increasing the probability of connection success.
[6] This algorithm is a distributed approach that is dependent upon a large amount of communication to use global information to always pick the shortest available path and wavelength.
The purpose of each counter is to determine which issue is a bigger factor in blocking: Path and wavelength availability or Quality requirements.
The algorithm chooses routes differently based upon the larger issue.
The multi-probing approach, which the paper names ALT-AQoS (alternate AQoS) is a simple extension upon the same basic idea.
There are several other wavelength assignment algorithms: Least Used, Most Used, Min Product, Least Loaded, Max Sum,[8] and Relative Capacity Loss.
Min Product, Least Loaded, Max Sum, and Relative Capacity Loss all try to choose a wavelength that minimizes the probability that future requests will be blocked.
An alternate approach to selecting a route and wavelength separately is to consider them jointly.
The approximation techniques usually aren't very useful either, as they will require centralized control and, usually, predefined traffic demands.
Additional constraints can be added and the process repeated indefinitely using a branch and bound approach.
In [10] the authors report on the algorithm which can be used to solve efficiently and optimally a constrained RWA problem.
In [11] the authors report on the generalized Dijkstra algorithm, which can be used to efficiently and optimally solve the RWA, RSA, and the routing, modulation, and spectrum assignment (RMSA) problems, without the limit on the path length.