TEAP: Traffic Engineering and ALR policy based Power-aware solutions for green routing and planning problems in backbone networks

Abstract Enormous and ever-increasing energy consumption in the Internet and the burgeoning global GreenHouse Gas (GHG) emission that come with it have been crucial issues for the past few years due to an exponential traffic growth and a rapid expansion of communication infrastructures worldwide. In this paper, we target Routing and Planning problems of Green Networking with bundled links referred to as RPGN by leveraging Traffic Engineering (TE) and Adaptive Link Rate (ALR) policy jointly to investigate the power-saving potentialities and effective applicability in the backbone networks. We formulate RPGN as a non-linear multi-commodity flow model and develop green heuristics-TE and ALR policy based Power-aware heuristics (TEAP) to solve it. We have investigated and compared different characterizations of the solutions to RPGN by evaluating network power saving ratio, rate level duration distribution, mean rate switching times and mean running time, under different real backbone network topology scenarios. Our results indicate the different power-saving potential of these solutions once applied in the backbone network.