Wenkai Dai, M. Dinitz, Klaus-Tycho Foerster, Stefan Schmid
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引用次数: 2
摘要
新兴的可重构光通信技术使需求感知网络成为可能:这种网络的静态拓扑结构可以通过针对网络服务的流量模式优化的需求感知链路来增强。本文研究了如何在需求感知网络中对拓扑和路由进行联合优化,使拥塞最小化的算法问题。我们沿着两个维度研究这个问题:(1)流是可分割的还是不可分割的,以及(2)混合拓扑上的路由是否被隔离,即,流是否必须专门使用静态网络或需求感知连接。对于可分割和隔离路由,我们证明了该问题在一般情况下是2-逼近的,但即使对于由二部需求图引起的一致需求也是apx -困难的。对于不可分割和隔离路由,我们展示了多项式时间近似算法的上界为O (log m/ log log m)和下界为Ω (log m/ log log m),其中m是静态链路的数量。在可分裂的情况下。(不可分割的)和非隔离的路由,甚至对于单个源的需求(如:,除非P=NP,否则不能比Ω (c max /c min)更好地逼近问题,其中c max (resp。, c min)表示最大值。(最小)容量。它仍然是np困难的统一能力,但可以有效地解决单一商品和统一的能力。
Brief Announcement: Minimizing Congestion in Hybrid Demand-Aware Network Topologies
Emerging reconfigurable optical communication technologies enable demand-aware networks: networks whose static topology can be enhanced with demand-aware links optimized towards the traffic pattern the network serves. This paper studies the algorithmic problem of how to jointly optimize the topology and the routing in such demand-aware networks, to minimize congestion. We investigate this problem along two dimensions: (1) whether flows are splittable or unsplittable, and (2) whether routing on the hybrid topology is segregated or not, i.e., whether or not flows either have to use exclusively either the static network or the demand-aware connections. For splittable and segregated routing, we show that the problem is 2-approximable in general, but APX-hard even for uniform demands induced by a bipartite demand graph. For unsplittable and segregated routing, we show an upper bound of O (log m/ log log m ) and a lower bound of Ω (log m/ log log m ) for polynomial-time approximation algorithms, where m is the number of static links. Under splittable (resp., unsplittable) and non-segregated routing, even for demands of a single source (resp., destination), the problem cannot be approximated better than Ω ( c max /c min ) unless P=NP, where c max (resp., c min ) denotes the maximum (resp., minimum) capacity. It is still NP-hard for uniform capacities, but can be solved efficiently for a single commodity and uniform capacities.