Routing algorithm for multiple unsplittable flows between two cloud sites with QoS guarantees

Erdal Akin, T. Korkmaz
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引用次数: 4

Abstract

Large scale cloud applications may require users to get multiple resources (e.g., VMs, storage) from different sites and simultaneously connect each pair of resources by a path that can satisfy certain Quality-of-Service (QoS) requirements. Finding such paths with the bandwidth constraint is known as the unsplittiable flow problem, which is shown to be NP-hard. Accordingly, various approximations as well as heuristic algorithms are proposed to maximize the amount of routed flows. However, the existing solutions suffer from either low performance in maximizing the amount of routed flows or excessive computation time. In this paper, we propose a new efficient heuristic for a special case, where multiple flows are sent between two cloud sites. Our heuristic algorithm first determines level cuts, set of links that are at same distance from source to destination, whose removal disconnects the source and destination. It then uses randomized best fit algorithm for assigning the flows to the links in the possible bottleneck cuts, which are the level cuts whose total available bandwidth is less then (1+γ) times the total requested bandwidth. It finally tries to forward flows between the level cuts using bandwidth constrained shortest path algorithm per flow. We demonstrate the efficiency of our heuristic using simulation.
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具有QoS保证的两个云站点间多条不可分割流的路由算法
大规模的云应用程序可能需要用户从不同的站点获取多个资源(例如,虚拟机、存储),并通过能够满足某些服务质量(QoS)要求的路径同时连接每对资源。在带宽约束下寻找这样的路径被称为不可分割流问题,这被证明是np困难的。因此,提出了各种近似和启发式算法来最大化路由流的数量。然而,现有的解决方案在最大化路由流数量方面存在性能低下或计算时间过多的问题。在本文中,我们提出了一种新的高效启发式算法,用于两个云站点之间发送多个流的特殊情况。我们的启发式算法首先确定水平切割,即从源到目标距离相同的一组链接,其移除将断开源和目标。然后,它使用随机最佳拟合算法将流分配到可能的瓶颈切割中的链接,瓶颈切割是总可用带宽小于总请求带宽的(1+γ)倍的水平切割。最后,它尝试在每个流之间使用带宽约束的最短路径算法转发流。我们用仿真来证明启发式算法的有效性。
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