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.

在过去的几年中，由于流量的指数级增长和全球通信基础设施的迅速发展，互联网上的能源消耗量不断增加，随之而来的迅速增长的全球温室气体（GHG）排放已成为至关重要的问题。在本文中，我们通过结合流量工程（TE）和自适应链路速率（ALR）策略共同研究骨干网中的节能潜力和有效适用性，针对具有捆绑链路的RPGN的绿色网络的路由和规划问题进行了研究。 。我们将RPGN公式化为非线性多商品流模型，并开发基于绿色启发式技术的TE和ALR策略基于Power-ware启发式技术（TEAP）来解决该问题。通过评估在不同的实际骨干网拓扑方案下的网络节电率，速率等级持续时间分布，平均速率切换时间和平均运行时间，我们已经研究并比较了RPGN解决方案的不同特性。我们的结果表明，这些解决方案一旦应用于骨干网络，将具有不同的节电潜力。