A cross-layer optimization based integrated routing and grooming algorithm for green multi-granularity transport networks

With the development of IP networks and intelligent optical switch networks, the backbone network tends to be a multi-granularity transport one. In a multi-granularity transport network (MTN), due to the rapid growth of various applications, the scale and complexity of network devices are significantly enhanced. Meanwhile, to deal with bursty IP traffic, the network devices need to provide continuous services along with excessive power consumption. It has attracted wide attention from both academic and industrial communities to build a power-efficient MTN. In this paper, we design an effective node structure for MTN. Considering the power savings on both IP and optical transport layers, we propose a mathematical model to achieve a cross-layer optimization objective for power-efficient MTN. Since this optimization problem is NP-hard (Hasan et al. (2010)  [11]) and heuristic or intelligent optimization algorithms have been successfully applied to solve such kinds of problems in many engineering domains (Huang et al. (2011)  [13], Li et al. (2011)  [17] and Dong et al. (2011)  [5]), a G  reen integrated R$R$outing and Grooming algorithm based on Biogeography-Based Optimization (Simon (2008)  [23]) (GRG_BBO) is also presented. The simulation results demonstrate that, compared with the other BBO based and state-of-the-art power saving approaches, GRG_BBO improves the power savings at a rate between 2%–15% whilst the high-level multi-user QoS (Quality of Services) satisfaction degree (MQSD) is guaranteed. GRG_BBO is therefore an effective technique to build a power-efficient MTN.