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On the feasibility of using hybrid evolutionary dynamic optimization for optimal monitor selection in dynamic communication networks

机译:在动态通信网络中使用混合进化动态优化进行最优监控器选择的可行性

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In this paper, we propose a technique to optimize dynamic communication network topologies. Due to recent developments in virtualization technologies like hardware virtualization, software defined networking (SDN), and network function virtualization (NFV), a completely new way of automated network optimization becomes possible. We propose a technique to enable hybrid evolutionary dynamic optimization of networking infrastructures based on changes to its topology or state. Networks have numerous, partially competing, attributes, which might be subject to optimization. Here, we focus exemplarily on the problem of finding the optimal amount and position of monitors in a computer network in order to monitor the whole network traffic. This dynamic monitor selection problem is generalizable to the well-known NP-complete vertex cover problem. Experiments are conducted on different real-world networks using an evolutionary search heuristic. We studied the behavior of the proposed approach using 3 different change levels of the problem instances. Experimental results show that our proposed approach provides network configurations having a sufficiently high quality in reasonable time for all problem instances and change levels. Thus, using the proposed technique may be one helpful step towards an automated self-adapting network optimization.
机译:在本文中,我们提出了一种优化动态通信网络拓扑的技术。由于诸如硬件虚拟化,软件定义网络(SDN)和网络功能虚拟化(NFV)等虚拟化技术的最新发展,一种全新的自动化网络优化方法成为可能。我们提出了一种技术,该技术可基于其拓扑结构或状态的更改实现网络基础结构的混合进化动态优化。网络具有许多可能会进行优化的部分竞争的属性。在这里,我们示例性地关注于发现监视器数量在计算机网络中的最佳数量和位置以便监视整个网络流量的问题。此动态监视器选择问题可推广到众所周知的NP-完全顶点覆盖问题。使用进化搜索启发法在不同的现实世界网络上进行实验。我们使用问题实例的3个不同更改级别研究了所提出方法的行为。实验结果表明,我们提出的方法可为所有问题实例和变化级别在合理的时间内提供具有足够高质量的网络配置。因此,使用提出的技术可能是迈向自动自适应网络优化的一个有用步骤。

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