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Provisioning in transport networks using power-aware resource allocation algorithms

机译:使用电源感知资源分配算法在传输网络中进行配置

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Transport technologies such as Optical Transport Network and Synchronous Digital Hierarchy are widely used in access and core networks to carry different types of traffic. These technologies define a hierarchy for multiplexing lower rate traffic containers onto higher rate traffic containers. Provisioning refers to the process of allocation of resources to meet a given traffic demand. Due to the continued expansion of transport networks, the power consumption increases, becoming a bottleneck for further expansion. Power-aware allocation ofresources to traffic demands will enable power-efficient operation and help in minimizing the capital and operational costs of the network. In this paper, the power consumed by a switch matrix having the capability to switch different traffic containers at different amounts is considered. The objective is to minimize the number of network elements deployed in the network by allocating resources to traffic demands in power-efficient ways. A formulation based on Integer Linear Programming is first presented; later, four different heuristic approaches,based on how higher order trails are used and the use of grooming, are proposed. It is found that the grooming based heuristics perform better in terms of the total power consumption relative to the weighted number of requests accepted. In the grooming heuristics, threshold on link utilization is set to determine when grooming is to be done, and for the same performance evaluation, it is found that the threshold of 80% gives better results.
机译:诸如光传输网络和同步数字体系的传输技术广泛用于接入和核心网络中,以承载不同类型的流量。这些技术定义了用于将较低速率的业务量容器多路复用到较高速率的业务量容器的层次结构。调配是指分配资源以满足给定流量需求的过程。由于运输网络的不断扩展,功耗增加,成为进一步扩展的瓶颈。功率感知型资源分配给流量需求将使功率高效运行并有助于最小化网络的资本和运营成本。在本文中,考虑了具有以不同量切换不同交通集装箱的能力的切换矩阵所消耗的功率。目的是通过以节能方式将资源分配给流量需求,以最大程度地减少网络中部署的网络元素的数量。首先提出了基于整数线性规划的公式。后来,提出了四种不同的启发式方法,这些方法基于如何使用更高阶的路径和修饰来使用。发现相对于接受的请求的加权数量,基于整理的启发式方法在总功耗方面表现更好。在修饰启发式方法中,设置链接利用率的阈值来确定何时进行修饰,并且对于相同的性能评估,发现阈值80%会给出更好的结果。

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