PARALLEL IMPLEMENTATION OF CROSS-LAYER OPTIMIZATION - A PERFORMANCE EVALUATION BASED ON SWARM INTELLIGENCE

In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO) within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO) in comparison with the traditional approach of Single-Layer Optimization (SLO). In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP) for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.