A novel adjustable defragmentation algorithm in elastic optical networks

Abstract

Fragmentation is one of the challenges in elastic optical networks (EONs), and therefore, solutions to deal with this challenge are important. Defragmentation is one of the reactive methods that is used after the occurrence of fragmentation. Defragmentation reduces fragmentation by rerouting and re-establishing someexisting connections. Fragmentation directly affects the demand blocking probability (DBP) and improves bandwidth efficiency. However, a large number of re-routings may decrease the quality of service. The starting time of the defragmentation algorithm for execution is also of great importance. In this article, a new algorithm is presented for defragmentation, called Adjustable Defragmentation (Adj-Defrag). In Adj-Defrag, the fragmentation problem in the network is defined as an integer nonlinear programming model based on the Golden metric, and then the desired items are determined optimally based on a heuristic algorithm and a meta-heuristic solution. This algorithm is compared with different and the results of the performance evaluation demonstrate its effectiveness in improving various network performance parameters.