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

机译:绿色多粒度传输网络的基于跨层优化的集成路由和修饰算法

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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 Green integrated Routing 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.
机译:随着IP网络和智能光交换网络的发展,骨干网倾向于成为一种多粒度的传输方式。在多粒度传输网络(MTN)中,由于各种应用程序的快速增长,网络设备的规模和复杂性得到了显着提高。同时,为了处理突发的IP流量,网络设备需要提供连续的服务以及过多的功耗。建立高效的MTN吸引了学术界和工业界的广泛关注。在本文中,我们为MTN设计了有效的节点结构。考虑到IP和光传输层上的功率节省,我们提出了一个数学模型来实现省电MTN的跨层优化目标。由于该优化问题是NP难的(Hasan等人(2010)[11]),启发式或智能优化算法已成功应用于许多工程领域中的此类问题(Huang等人(2011)[13] ],Li等人(2011)[17]和Dong等人(2011)[5]),一种基于基于生物地理的优化的绿色集成路由和整理算法(Simon(2008)[23])(GRG_BBO)还介绍了。仿真结果表明,与其他基于BBO的最新省电方法相比,GRG_BBO以2%-15%的速率提高了节电率,同时实现了高级多用户QoS(Quality of服务)满意度(MQSD)得到保证。因此,GRG_BBO是一种构建节能MTN的有效技术。

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