Optimization in Elastic Optical Networks

Data traffic demands are increasing annually. The networks need to respond to these increased demands. To do end, there are a few directions for the network operators or researchers: first is to increase the size of the networks or increasing the equipment. This direction in some cases is inevitable, however it some aspects of this strategy are inconsistent with current user expectations. Using more equipment increases the costs of the network and puts extra cost on the end users and the networks operators. Also, it does not guarantee any improvement in the quality of service which is another major expectation of the end users. A second approach being undertaken by the research community is to invent more efficient equipment such as, less noisy amplifiers, optical wavelength convertors, etc. This has been of the major direction to have better networks. Finally, adjusting the communication and networking protocols and standards have always been one of the major considerations because of its low cost and effectiveness. Optical networks currently form the major parts of the current communication infrastructures. Especially in tier one and tier 2 networks. Wavelength division multiplexing and Elastic Optical Networks are two existing Networking framework for optical networks. In this research by focusing on the Network layer, we review the major algorithms existing in these networks, and then some new strategies to overcome their main issues are presented along with experimental results. The main challenge behind resource allocation for different traffic demands is called routing , modulation level and spectrum allocation (RMSA). This problem introduces more flexibilities compared to WDM similar problem. At the same time, this higher flexibility increases the complexity of any algorithm to solve RMSA. In this research, first we proposed a sub-tree model to serve multicast demands. In this model we assume the flexibility of choosing routing, spectrum and modulation level for each single subtree and considered a limited number of regeneration along the path for each subtree. Then using an Integer Liner Programming, we mathematically found the upper bound of this new model and showed that it will effectively increase the capacity of the networks. For these models we also came up with fast algorithms, which can be used in practice in provisioning phase of the network. To show the closeness of the algorithm to the proposed model we compared their performance with ILP equivalent of the model. To conduct our experiments we used three optical networks, two of which were real size big networks and the other an exemplary small five node network. The results showed that, as we expected, the proposed model is particularly effective, with an increase capacity of close 50 percent, in large networks as the flexibilities in the model, including the regeneration and subtree, will result in using more efficient modulations and less frequency slices and less spectrum defragmentation, all provided that, on average, the distance between the source and the destinations is close to the maximum reach of the least efficient modulation techniques that are available the sender nodes of the network.