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Efficient Heuristic for Placing Monitors on Flow Networks

机译:高效启发式在流量网络上放置监视器

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Network flow monitoring is a crucial procedure for optimizing the network infrastructure for better application performance or security issue. In this paper, we address the problem of Flow Edge-Monitoring (denoted as FEM), for which we need to find k (k > 0) edges in an undirected graph G = (V, E), so that if we place k flow monitors on these edges to measure the flow along them, we can maximize the total number of edges for which the flow information can be uniquely determined according to the flow conservation property. This problem has been proved to be NP-hard, and previously reported approach suffers from higher running time complexity. In this paper, we develop efficient heuristic algorithm ALGHEU to significantly accelerating the FEM problem without compromising on solution quality, i.e. Without obviously increasing the number of required monitors. We also customize and Ant Colony algorithm, which runs slow but produce near optimal solution, to evaluate our heuristic algorithm ALGHEU. The experimental results show that, the proposed approach can significantly accelerate the previous approach by 47.67 times, while the solution quality remains nearly as good as the previous algorithm.
机译:网络流量监控是优化网络基础架构以获得更好的应用程序性能或安全问题的重要程序。在本文中,我们解决了流边缘监测(表示为FEM)的问题,我们需要在无向图G =(v,e)中找到k(k> 0)边,使得如果我们放置k这些边缘上的流动监视器为了测量沿它们的流量,我们可以通过根据流动节约特性唯一地确定流动信息的边缘的总数。此问题已被证明是NP - 硬,先前报告的方法遭受了更高的运行时间复杂性。在本文中,我们开发了高效的启发式算法Algheu,在不影响溶液质量的情况下显着加速有限元问题,即没有显然增加所需监视器的数量。我们还定制和蚁群算法,运行速度慢但在最佳解决方案附近产生,以评估我们的启发式算法Algheu。实验结果表明,所提出的方法可以显着加速以前的方法47.67次,而溶液质量几乎与先前的算法一样好。

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