Optimizing network performance and carbon offset through opportunistic reclustering

Abstract

We have viewed an enterprise information network as a flexible topology, where each node seeks the opportunity to join a cluster having high association with its nodes to reduce the external bandwidth and thereby producing less carbon. The opportunistic clustering is modeled as an optimization problem and we utilized heuristics such as genetic algorithm (GA) and simulated annealing (SA) to search for the best opportunistic topology. We have proposed redesign operations such as move and swap nodes to change the existing topology into an opportunistic topology. Also, we defined an opportunistic factor ranging from +1 to -1 to measure the gain in bandwidth and the amount of reduction in carbon emissions. Our simulation results demonstrate positive opportunistic factor in individual clusters from 0.4 to 0.7 in move operation, thereby showing a total bandwidth gain of 21% and 19% within GA and SA, respectively. In the swap operation, the opportunistic factor reached a maximum of 0.3, thereby showing a bandwidth gain of 9.7% and 5.1% within GA and SA, respectively. It is also observed that the optimization within GA outperforms SA in offsetting carbon emission with a maximum of 22%.

Enterprise Workload Management through Ant Colony Optimization

“…In our earlier work [13][18], we performed redesign operations on enterprise networks utilizing Simulated Annealing, to minimize the backbone traffic. In this paper, we have utilized ACO to re-optimize BEN with new clusters for managing the increased volume of traffic at the backbone.…”