Optimal Algorithms for Load Balancing in Optical Burst Switching Networks

Data packet drop can happen in Optical Burst-Switched (OBS) when two data bursts are competing on the same wavelength. Recently, many techniques have been developed to solve this problem but they do not consider the congestion. Also, it is necessary to balance the load system in the OBS network. The Ant Colony Optimization (ACO) technique can be applied to determine the straight and the safest route. However, the ACO technique raises both power utilization as well as the execution time. In this study, Cuckoo Search (CS) and ACO methods based approach is proposed to avoid the congestion and load balancing in the OBS network. This strategy evaluates the intensity of hotspot data and then launches the congestion rate optimization process that depends on their load situations. The congestion rate optimization represents the available bandwidth, data distribution rate, queue size, and access rate, and also these factors are optimized through the CS technique. The fitness utility in the CS technique adjusts the distribution rate in the OBS network, and the proposed ACO technique solves the energy utilization problem. The simulation results proved that the presented strategy evades both the end-to-end delay as well as the possibility of packet drop.