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Path Decomposition Under a New Cost Measure with Applications to Optical Network Design

机译:通过应用于光网络设计的新成本测量下的路径分解

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We introduce a problem directly inspired by its application to DWDM (dense wavelength division multiplexing) network design. We are given a set of demands to be carried over a network. Our goal is to choose a route for each demand and to decompose the network into a collection of edge-diajoint simple paths. These paths are called optical line systems. The cost of routing one unit of demand is the number of line systems with which the demand route overlaps; our design objective is to minimize the total cost over all demands. This cost metric is motivated by the need to avoid O-E-0 (optic.al-electrical-opticat) conversions in optical transmission as well as to minimize the expense of the equipment necessary to carry the traffic. For given line systems, it is easy to find the optimal demand routes. On the other hand, for given demand routes designing the optimal line systems can be NP-hard. We first present a 2-approximalion for general network topologies. As optical networks often have low node degrees, we also offer an algorithm that finds the optimal solution for the special case in which the node degree is at most 3. If neither demand routes nor line systems are fixed, the situation becomes much harder. Even for a restricted scenario on a 3-regular Hamiltonian network, no efficient algorithm can guarantee a constant approximation better than 2. For general topologies, we offer a simple algorithm with an O(log K)- and an O(log n)-approximation where K is the number of demands and n is the number of nodes. For rings, a common special topology, we offer a 3/2-approximation.
机译:我们介绍了它在DWDM(密集波分复用)​​网络设计中的应用直接启发的问题。我们被提供了一系列要求通过网络进行。我们的目标是为每种需求选择一条路线,并将网络分解为边缘差别简单路径的集合。这些路径称为光学线路系统。路由一个单位的成本是需求路线重叠的线系数量;我们的设计目标是最大限度地减少所有需求的总成本。这种成本度量是有必要避免光传输中的O-E-0(光学元 - 电气 - OptiCat)转换的动力,并最大限度地减少承载流量所需的设备的费用。对于给定的线路系统,很容易找到最佳需求路由。另一方面,对于指定的需求路线设计最佳线路系统可以是NP-HARD。我们首先为一般网络拓扑提供2近似值。由于光网络经常具有低节点度,我们还提供一种算法,该算法可以找到最多的特殊情况的最佳解决方案3.如果不需要需求路由,情况都没有固定,则情况变得更加困难。即使对于3常规Hamiltonian网络上的受限制方案,也没有高效的算法可以保证比2更好的常见近似。对于一般拓扑,我们提供了一种具有O(log k)的简单算法 - 以及O(log n) - 近似值,其中k是需求次数,n是节点的数量。对于戒指,一个常见的特殊拓扑,我们提供3/2近似。

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